In the disclosed systems and methods for characterizing a cancer condition of a tissue in a subject, a computer system inputs information into an ensemble model. The information includes, for each respective class of radiomics features in a plurality of classes of radiomics features, a corresponding value for each respective radiomic feature in a corresponding plurality of radiomics features of the respective class of radiomics features obtained from a medical imaging dataset. The ensemble model comprises a plurality of component models. The computer system obtains as output from each respective component model in the plurality of component models a corresponding component prediction for the cancer condition, thereby obtaining a plurality of component predictions for the cancer condition. The computer system combines the plurality of component predictions to obtain as output of the ensemble model a characterization of the cancer condition.
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 6/00 - Apparatus or devices for radiation diagnosisApparatus or devices for radiation diagnosis combined with radiation therapy equipment
Systems and methods for phenotyping clinical data are provided. The method includes obtaining episodic records comprising unstructured clinical data from an electronic medical record (EMR) or electronic health record (EHR) for patients. The method also includes filtering the episodic records by language pattern recognition to identify episodic records that each includes an expression related to a clinical condition. The method also includes splitting each episodic record to obtain snippets comprising tokens. The method also includes predicting if an episodic record represents an instance of the clinical condition using a trained classifier. The trained classifier includes an aggregation function that aggregates the snippets to output a corresponding representation for the episodic record, and an interpretation function that interprets the corresponding representation to output a corresponding prediction for whether the episodic record represents an instance of the clinical condition.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
3.
METHOD AND PROCESS FOR PREDICTING AND ANALYZING PATIENT COHORT RESPONSE, PROGRESSION, AND SURVIVAL
A system and method for analyzing a data store of de-identified patient data to generate one or more dynamic user interfaces usable to predict an expected response of a particular patient population or cohort when provided with a certain treatment. The automated analysis of patterns occurring in patient clinical, molecular, phenotypic, and response data, as facilitated by the various user interfaces, provides an efficient, intuitive way for clinicians to evaluate large data sets to aid in the potential discovery of insights of therapeutic significance.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
Downloadable and recorded computer software and mobile applications for use in providing recommendations for patient treatment and care in the fields of cardiology, oncology, and neurology; downloadable and recorded computer software and mobile applications featuring artificial intelligence for machine learning and predictive analytics to support clinical data platforms for patient diagnosis and treatment in the fields of cardiology, oncology, and neurology Software as a service (SAAS) services featuring online non-downloadable software for use in providing recommendations for patient treatment and care in the fields of oncology and neurology; software as a service (SAAS) services featuring software using artificial intelligence for machine learning and predictive analytics to support clinical data platforms for patient diagnosis and treatment in the fields oncology and neurology Medical services; providing medical information in the fields of oncology and neurology
Compositions for enriching target nucleic acids, and methods of using the same, are provided. The composition includes a probe set and a plurality of nucleic acids. The probe set includes a first set of probes comprising a first plurality of probe species, each probe species targeting a respective genomic region in a first plurality of genomic regions and present in the composition at a first average molar concentration. The probe set further includes a second set of probes comprising a second plurality of probe species, each probe species targeting a respective genomic region in a second plurality of genomic regions and present in the composition at a second average molar concentration that is from five to eight times greater than the first average concentration. The plurality of nucleic acids comprises cell-free nucleic acids from a biological sample of a subject, or nucleic acids prepared therefrom.
C12Q 1/6816 - Hybridisation assays characterised by the detection means
C12Q 1/6827 - Hybridisation assays for detection of mutation or polymorphism
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
6.
Artificial Intelligence Based Cardiac Event Predictor Systems and Methods
A method and system for determining cardiac disease risk from electrocardiogram trace data is provided. The method includes receiving electrocardiogram trace data associated with a patient, the electrocardiogram trace data having an electrocardiogram configuration including a plurality of leads. One or more leads of the plurality of leads that are derivable from a combination of other leads of the plurality of leads are identified, and a portion of the electrocardiogram trace data does not include electrocardiogram trace data of the one or more leads. The portion of the electrocardiogram data is provided to a trained machine learning model, to evaluate the portion of the electrocardiogram trace data with respect to one or more cardiac disease states. A risk score reflecting a likelihood of the patient being diagnosed with a cardiac disease state within a predetermined period of time is generated by the trained machine learning model based on the evaluation.
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/318 - Heart-related electrical modalities, e.g. electrocardiography [ECG]
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
A61B 5/28 - Bioelectric electrodes therefor specially adapted for particular uses for electrocardiography [ECG]
7.
METHOD AND PROCESS FOR PREDICTING AND ANALYZING PATIENT COHORT RESPONSE, PROGRESSION, AND SURVIVAL
A system and method for analyzing a data store of de-identified patient data to generate one or more dynamic user interfaces usable to predict an expected response of a particular patient population or cohort when provided with a certain treatment. The automated analysis of patterns occurring in patient clinical, molecular, phenotypic, and response data, as facilitated by the various user interfaces, provides an efficient, intuitive way for clinicians to evaluate large data sets to aid in the potential discovery of insights of therapeutic significance.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
8.
METHODS AND SYSTEMS FOR ACCURATE GENOTYPING OF REPEAT POLYMORPHISMS
Methods, systems, and software are provided for determining a genotype for a genomic locus comprising a tandem repeat having contiguous repeat units. Sequence reads that encompass and map to the tandem repeat are obtained. A repeat count distribution for the number of repeat units in the reads is determined. Sets of adjustment factors are obtained, each set (i) corresponding to a different allele having a different repeat unit count and (ii) including corresponding adjustment factors for a range of repeat unit counts. Candidate genotypes correspond to combinations of two alleles in a plurality of candidate alleles. Each candidate genotype is assigned a likelihood based at least in part on, for each allele in the candidate genotype: (i) a proportion of sequence reads having the repeat count corresponding to the allele and (ii) an adjustment factor from the corresponding set. The candidate genotype having the highest likelihood is selected.
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
G16B 30/00 - ICT specially adapted for sequence analysis involving nucleotides or amino acids
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
9.
Artificial intelligence based cardiac event predictor systems and methods
A method and system for predicting the likelihood that a patient will suffer from a cardiac event is provided. The method includes receiving electrocardiogram data associated with the patient, providing at least a portion of the electrocardiogram data to a trained model, receiving a risk score indicative of the likelihood the patient will suffer from the cardiac event within a predetermined period of time from when the electrocardiogram data was generated, and outputting the risk score to at least one of a memory or a display for viewing by a medical practitioner or healthcare administrator. The system includes at least one processor executing instructions to carry out the steps of the method.
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
A61B 5/318 - Heart-related electrical modalities, e.g. electrocardiography [ECG]
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
A61B 5/28 - Bioelectric electrodes therefor specially adapted for particular uses for electrocardiography [ECG]
G06K 9/62 - Methods or arrangements for recognition using electronic means
10.
METHODS AND SYSTEMS FOR DETECTING ALTERNATIVE SPLICING IN SEQUENCING DATA
The disclosure provides methods and systems for detecting alternative splicing variants in a patient sample. The methods involve comparison of splice junction data from RNA sequencing with principal splicing isoforms and identifying those sequences, identifying those sequences that do not match the principal splicing isoform. These sequences are categorized into alternative splicing events and documented. Optionally, previously identified target sequences can be utilized in the comparison where the method seeks sequences which do match. Other methods and systems of the present disclosure include those for building a splice profile of alternative splicing variants for a patient sample and those for developing a companion diagnostic test for a treatment method of a disease based on the presence or absence of alternative splicing variants in a patient sample.
The disclosure provides methods and systems for detecting alternative splicing variants in a patient sample. The methods involve comparison of splice junction data from RNA sequencing with principal splicing isoforms and identifying those sequences, identifying those sequences that do not match the principal splicing isoform. These sequences are categorized into alternative splicing events and documented. Optionally, previously identified target sequences can be utilized in the comparison where the method seeks sequences which do match. Other methods and systems of the present disclosure include those for building a splice profile of alternative splicing variants for a patient sample and those for developing a companion diagnostic test for a treatment method of a disease based on the presence or absence of alternative splicing variants in a patient sample.
A method for characterizing cancer organoid response to an immune cell based therapy, includes providing a panel of different combinations of cancer organoid cells and immune cells to culturing wells and culturing the different combination under conditions that support organoid growth. Brightfield and corresponding fluorescence images of the culturing wells are captured and provided to one or more trained machine learning algorithms that identify and distinguish cancer organoid cells from immune cells and characterize cancer organoid morphology changes caused by an immune cell based therapies, from which an analytical report including a characterization of cancer organoid cell death caused by the immune cell based therapy is provided.
Processes are provided for identifying somatic variants in a human leukocyte antigen (HLA) gene in a subject using analysis of next generation sequencing (NGS) data. The processes include aligning HLA sequence read data for normal samples and the HLA sequence data for a tumor sample to a patient specific HLA reference genome and performing a variant calling process on filtered aligned read data and determining somatic variants of the HLA class. A report may be generated of somatic variants of the HLA gene annotated for functional effect.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
14.
TRANSLATING AI ALGORITHMS FROM 12-LEAD CLINICAL ECGS TO PORTABLE AND CONSUMER ECGS WITH FEWER LEADS
A method includes the step of receiving electrocardiogram (ECG) data associated with a plurality of patients and an electrocardiogram configuration including a plurality of leads and a time interval. The electrocardiogram data includes, for each lead included in the plurality of leads, voltage data associated with at least a portion of the time interval. The method also includes training an artificial intelligence model on the ECG data, tuning the artificial intelligence model using data from a device having fewer leads than the plurality of leads, and evaluating the artificial intelligence model on additional data received from the ECG data.
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
15.
DETECTION OF HUMAN LEUKOCYTE ANTIGEN LOSS OF HETEROZYGOSITY
Processes are provided for detecting loss of heterozygosity of Human Leukocyte Antigen (HLA) in a subject using analysis of next generation sequencing (NGS) data. The processes include aligning NGS data and identifying unmapped and mapped reads, updating reference data, and feeding one or more sequence reads to an HLA typing process for identifying candidate HLA alleles and feeding HLA type data to a loss of heterozygosity (LOH) modeling process for determining a LOH status for each HLA allele. A report may be generated of the LOH statuses for each of HLA allele.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
16.
ECG-BASED CARDIOVASCULAR DISEASE DETECTION SYSTEMS AND RELATED METHODS
A method for determining cardiology disease risk from electrocardiogram trace data and clinical data includes receiving electrocardiogram trace data associated with a patient, receiving the patient's clinical data, providing both sets of data to a trained machine learning composite model that is trained to evaluate the data with respect to each disease of a set of cardiology diseases including three or more of cardiac amyloidosis, aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid regurgitation, abnormal reduced ejection fraction, or abnormal interventricular septal thickness, generating, by the model and based on the evaluation, a composite risk score reflecting a likelihood of the patient being diagnosed with one or more of the cardiology diseases within a predetermined period of time from when the electrocardiogram trace data was generated, and outputting the composite risk score to at least one of a memory or a display.
A method and system for predicting the likelihood that a patient will suffer from a cardiac event is provided. The method includes receiving electrocardiogram data associated with the patient, providing at least a portion of the electrocardiogram data to a trained model, receiving a risk score indicative of the likelihood the patient will suffer from the cardiac event within a predetermined period of time from when the electrocardiogram data was generated, and outputting the risk score to at least one of a memory or a display for viewing by a medical practitioner or healthcare administrator. The system includes at least one processor executing instructions to carry out the steps of the method.
A method and system for predicting the likelihood that a patient will suffer from a cardiac event is provided. The method includes receiving electrocardiogram data associated with the patient, providing at least a portion of the electrocardiogram data to a trained model, receiving a risk score indicative of the likelihood the patient will suffer from the cardiac event within a predetermined period of time from when the electrocardiogram data was generated, and outputting the risk score to at least one of a memory or a display for viewing by a medical practitioner or healthcare administrator. The system includes at least one processor executing instructions to carry out the steps of the method.
Systems and methods are provided for balancing a probe set for enriching a plurality of genomic loci. A nucleic acid probe set containing pools of nucleic acid probe species is obtained. Each probe species aligns to a different subsequence of a respective locus and includes proportions of a capture moiety conjugated version and a capture moiety-free version. Each probe species in a pool aligns to a portion of the genome that is at least 100 nucleotides away from any other probe species in the pool. Each pool in the probe set is separately analyzed against reference nucleic acid samples to obtain recovery rates and identify probe species that do not satisfy a minimum or a maximum recovery rate threshold. An adjusted version of a final design for the probe set is established by adjusting proportions of capture moiety conjugated and capture moiety-free versions for the identified probe species.
The present disclosure provides methods for treating a subject that has been diagnosed with cancer. The methods utilize longitudinal genomic testing to monitor the progression of a subject's cancer over time. Specifically, the methods involve comparing sequencing data collected from paired tumor-normal samples and liquid biopsies to sequencing data collected from the same sample types at an earlier time point to identify changes in the tumor genomic profile.
C12Q 1/6895 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms for plants, fungi or algae
21.
METHODS AND SYSTEMS FOR mRNA BOUNDARY ANALYSIS IN NEXT GENERATION SEQUENCING
Methods, systems, and software are provided for detecting gene fusions in a subject with a cancer condition through mRNA boundary analysis of next generation sequencing of a transcriptome or relevant part thereof. Methods, systems, and software are provided for detecting splice variants in a subject with a cancer condition through mRNA boundary analysis of next generation sequencing of a transcriptome or relevant part thereof. Methods, systems, and software are provided for evaluating the complexity of an RNA-seq sequencing reaction through mRNA boundary analysis. Generally, the methods described herein include obtaining sequences of mRNA molecules for a plurality of genes in a sample of a subject. For each gene, an RNA boundary distribution including relative abundance value for each respective RNA boundary sub-sequence of the gene is determined from the plurality of sequences. These abundance values are evaluated using one or more models to provide the analyses described herein.
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
22.
SYSTEMS AND METHODS FOR JOINT LOW-COVERAGE WHOLE GENOME SEQUENCING AND WHOLE EXOME SEQUENCING INFERENCE OF COPY NUMBER VARIATION FOR CLINICAL DIAGNOSTICS
Methods, systems, and software are provided for determining copy number variation status of a subject. A first plurality of nucleic acid sequences generated by whole genome sequencing at an average depth of 0.5X to 5X is obtained from a first sample. A second plurality of nucleic acid sequences generated by panel-targeted sequencing is obtained from a second sample. A first mapped dataset is obtained by mapping the first plurality of sequences to positions within a reference genome for the species of the subject. A second mapped dataset is obtained by mapping the second plurality of sequences to positions within a reference construct for genomic regions targeted by the panel-targeted sequencing. A model is applied to all or a portion of the first mapped dataset and all or a portion of the second mapped dataset, or dimensionality reduction components thereof.
The present disclosure provides methods, systems, compositions, and kits for the high- throughput detection of multi-molecule biomarkers in a biological sample. The disclosed methods, systems, compositions, and kits utilize antibody-oligonucleotide tags to detect two or more molecules that are in close proximity.
C12Q 1/6818 - Hybridisation assays characterised by the detection means involving interaction of two or more labels, e.g. resonant energy transfer
C12Q 1/6848 - Nucleic acid amplification reactions characterised by the means for preventing contamination or increasing the specificity or sensitivity of an amplification reaction
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
C40B 30/04 - Methods of screening libraries by measuring the ability to specifically bind a target molecule, e.g. antibody-antigen binding, receptor-ligand binding
24.
PREDICTING TOTAL NUCLEIC ACID YIELD AND DISSECTION BOUNDARIES FOR HISTOLOGY SLIDES
A method for qualifying a specimen prepared on one or more hematoxylin and eosin (H&E) slides by assessing an expected yield of nucleic acids for tumor cells and providing associated unstained slides for subsequent nucleic acid analysis is provided.
The present disclosure provides methods, systems, compositions, and kits for the high-throughput detection of multi-molecule biomarkers in a biological sample. The disclosed methods, systems, compositions, and kits utilize antibody-oligonucleotide tags to detect two or more molecules that are in close proximity.
C12Q 1/68 - Measuring or testing processes involving enzymes, nucleic acids or microorganismsCompositions thereforProcesses of preparing such compositions involving nucleic acids
C12P 19/34 - Polynucleotides, e.g. nucleic acids, oligoribonucleotides
C12Q 1/6883 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
A method for associating published media with a subject includes receiving a cancerous biological specimen, sequencing it to obtain subject genomic data, identifying first and second alteration nomenclature matches to the subject genomic data in data extracted from first and second published media, applying a hierarchical rule set to the media based on the alteration nomenclature matches and one or more evidence metrics, the hierarchical rule set resulting in reporting a first treatment in the first medium and excluding reporting of a second treatment in the second medium despite a match between the second medium and subject disease states, identifying a reporting template based on the subject genomic data and the disease state, generating a report using the identified template, the report reporting treatments according to the hierarchical rule set, comparing the report to one or more approval criteria, and publishing the report when the approval criteria are satisfied.
Computer-implemented systems and methods are provided for supplying electrocardiograms and identified patient information to an artificial intelligence engine comprising a neural network configured with a fractional flow reserve prediction model and that predicts a calculated fractional flow reserve for the patient, from which a predicted occurrence of one or more cardiac events is determined.
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
28.
SYSTEMS AND METHODS FOR GENERATING HISTOLOGY IMAGE TRAINING DATASETS FOR MACHINE LEARNING MODELS
A system and method are provided for training and using a machine learning model to analyze hematoxylin and eosin (H&E) slide images, where the machine learning model is trained using a training data set comprising a plurality of unmarked H&E images and a plurality of marked H&E images, each marked H&E image being associated with one unmarked H&E image and each marked H&E image including a location of one or more molecules determined by analyzing a multiplex IHC image having at least two IHC stains, each IHC stain having a unique color and a unique target molecule. Predicted molecules and locations identified with the machine learning model result in an immunotherapy response class being assigned to the H&E slide image.
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
G16B 30/00 - ICT specially adapted for sequence analysis involving nucleotides or amino acids
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
29.
DETERMINATION OF CYTOTOXIC GENE SIGNATURE AND ASSOCIATED SYSTEMS AND METHODS FOR RESPONSE PREDICTION AND TREATMENT
Disclosed herein are systems, methods, and compositions for treating a subject diagnosed with, or suffering from cancer. In some embodiments, the method comprises determining whether a tumor sample from the subject includes a cytotoxic gene signature, and treating the subject based on the determination. In some embodiments, the subject has or is suspected of having a loss of heterozygosity in human leukocyte antigen (HLA) class I genes. In some embodiments, the therapy comprises one or more checkpoint inhibitors. In some embodiments, the cancer is colorectal, uterine, stomach, lung, skin, head or neck, or non-small cell lung carcinoma.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
G01N 33/574 - ImmunoassayBiospecific binding assayMaterials therefor for cancer
C12Q 1/68 - Measuring or testing processes involving enzymes, nucleic acids or microorganismsCompositions thereforProcesses of preparing such compositions involving nucleic acids
A method for transferring a dataset-specific nature of a first dataset with sequencing results for a first plurality of specimen to a second dataset with sequencing results for a second plurality of specimen includes receiving a first set of adaptation factors of the first dataset that include two or more eigenvectors, where the sequencing cannot be reconstructed from the first set of adaptation factors without access to the first dataset. The method also includes generating a second set of adaptation factors of the second dataset that include two or more eigenvectors of the second dataset. The method also includes generating an adapted second dataset by adapting the dataset-specific nature of the second dataset to the dataset-specific nature of the second dataset based at least in part on the first and second sets of adaptation factors, and providing the adapted second dataset to the first entity.
G16H 30/40 - ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
G06K 9/66 - Methods or arrangements for recognition using electronic means using simultaneous comparisons or correlations of the image signals with a plurality of references, e.g. resistor matrix references adjustable by an adaptive method, e.g. learning
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
Methods, systems, and software are provided for monitoring a cancer condition of a test subject. The method includes obtaining a liquid biopsy sample from the subject at a second time point, occurring after a first time point, containing cell-free DNA fragments. Low-pass whole genome methylation sequencing of the cell-free DNA fragments is performed to obtain nucleic acid sequences having a methylation pattern for a corresponding cell-free DNA fragment. The nucleic acid sequences are mapped to a location on a reference genome. Methylation metrics are determined based on the methylation patterns and mapped locations of the nucleic acid sequences. A circulating tumor fraction is estimated from the methylation metrics, and the estimate is compared to an estimate of the circulating tumor fraction for the test subject at the first time point.
Disclosed herein are systems, methods, and compositions useful for profiling T cell receptor (TCR) and B cell receptor (BCR) repertoire using next-generation sequencing (NGS) methods. In certain embodiments, the methods include enriching a sample for TCR/BCR RNA sequences, and determining the TCR/BCR profile of a subject using five different oligonucleotide pools. Also disclosed herein are systems and methods for diagnosing, treating, or predicting infection, disease, medical conditions, therapeutic outcome, or therapeutic efficacy based on the TCR/BCR profile data from a subject in need thereof.
C12Q 1/6881 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for tissue or cell typing, e.g. human leukocyte antigen [HLA] probes
34.
Collaborative artificial intelligence method and system
A method and system of audibly broadcasting responses to a user based on user queries about a specific patient molecular report, the method comprising receiving an audible query from the user to a microphone coupled to a collaboration device, identifying at least one intent associated with the audible query, identifying at least one data operation associated with the at least one intent, associating each of the at least one data operations with a first set of data presented on the molecular report, executing each of the at least one data operations on a second set of data to generate response data, generating an audible response file associated with the response data and providing the audible response file for broadcasting via a speaker coupled to the collaboration device.
G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 70/60 - ICT specially adapted for the handling or processing of medical references relating to pathologies
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
G16H 70/20 - ICT specially adapted for the handling or processing of medical references relating to practices or guidelines
G06F 3/0481 - Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
A system and method for analyzing a data store of de-identified patient data to generate one or more dynamic user interfaces usable to predict an expected response of a particular patient population or cohort when provided with a certain treatment. The automated analysis of patterns occurring in patient clinical, molecular, phenotypic, and response data, as facilitated by the various user interfaces, provides an efficient, intuitive way for clinicians to evaluate large data sets to aid in the potential discovery of insights of therapeutic significance.
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G06K 9/62 - Methods or arrangements for recognition using electronic means
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G06F 17/18 - Complex mathematical operations for evaluating statistical data
G16H 30/00 - ICT specially adapted for the handling or processing of medical images
G16H 80/00 - ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
G06F 18/22 - Matching criteria, e.g. proximity measures
G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
G06F 18/2115 - Selection of the most significant subset of features by evaluating different subsets according to an optimisation criterion, e.g. class separability, forward selection or backward elimination
G06F 18/243 - Classification techniques relating to the number of classes
This present disclosure relates to systems, methods, and compositions useful for profiling T cell receptor (TCR) and B cell receptor (BCR) repertoire using next-generation sequencing (NGS) methods. The present disclosure also relates to systems and methods for diagnosing, treating, or predicting infection, disease, medical conditions, therapeutic outcome, or therapeutic efficacy based on the TCR/BCR profile data from a subject in need thereof.
C07K 16/30 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants from tumour cells
The disclosure provides a method of generating an artificial fluorescent image of cells is provided. The method includes receiving a brightfield image generated by a brightfield microscopy imaging modality of at least a portion of cells included in a specimen, applying, to the brightfield image, at least one trained model, the trained model being trained to generate the artificial fluorescent image based on the brightfield image, receiving the artificial fluorescent image from the trained model
A system and method for analyzing a data store of de-identified patient data to generate one or more dynamic user interfaces usable to predict an expected response of a particular patient population or cohort when provided with a certain treatment. The automated analysis of patterns occurring in patient clinical, molecular, phenotypic, and response data, as facilitated by the various user interfaces, provides an efficient, intuitive way for clinicians to evaluate large data sets to aid in the potential discovery of insights of therapeutic significance.
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G06K 9/62 - Methods or arrangements for recognition using electronic means
G06F 17/18 - Complex mathematical operations for evaluating statistical data
G06N 5/00 - Computing arrangements using knowledge-based models
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 30/00 - ICT specially adapted for the handling or processing of medical images
G16H 80/00 - ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
Systems and methods are provided for predicting metastasis of a cancer in a subject. A plurality of data elements for the subject's cancer is obtained, including sequence features comprising relative abundance values for gene expression in a cancer biopsy of the subject, optional personal characteristics about the subject, and optional clinical features related to the stage, histopathological grade, diagnosis, symptom, comorbidity, and/or treatment of the cancer in the subject, and/or a temporal element associated therewith. One or more models are applied to the plurality of data elements, determining one or more indications of whether the cancer will metastasize. A clinical report comprising the one or more indications is generated.
Methods, systems, and software are provided for determining whether a subject is afflicted with an oncogenic pathogen. Nucleic acids from a biological sample of the subject are hybridized to a probe set that includes probes for human genomic loci and for genomic loci of oncogenic pathogens. Sequence reads of the hybridized nucleic acid are obtained and it's determined whether each sequence read aligns to a human reference genome. For each sequence read that fails to align to the human reference genome, it's determined whether the sequence read aligns to a reference genome of an oncogenic pathogen. Sequence reads that both (i) fail to align to the human reference genome and (ii) align to a reference genome of an oncogenic pathogen are tracked, thereby obtaining a sequence read count for the oncogenic pathogen. The sequence read count is used to ascertain whether the subject is afflicted with the oncogenic pathogen.
C12Q 1/689 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms for bacteria
C12Q 1/70 - Measuring or testing processes involving enzymes, nucleic acids or microorganismsCompositions thereforProcesses of preparing such compositions involving virus or bacteriophage
Methods, systems, and software are provided for validating a copy number variation, validating a somatic sequence variant, and/or determining circulating tumor fraction estimates using on-target and off-target sequence reads in a test subject. A copy number status annotation for a genomic segment is validated by applying a first dataset to a plurality of filters comprising a measure of central tendency bin-level sequence ratio filter, a confidence filter, and a measure of central tendency-plus-deviation bin-level sequence ratio filter. A somatic sequence variant is validated by comparing a variant allele fragment count for a candidate somatic sequence variant for a respective locus, against a dynamic variant count threshold for the locus in a respective reference sequence. A circulating tumor fraction is estimated based on a measure of fit between genomic segment-level coverage ratios and integer copy states across a plurality of simulated circulated tumor fractions.
A method and system for conducting genomic sequencing includes a first microservice for receiving an order from a physician to initiate an NGS of a patient's germ line specimen and somatic specimen using a targeted-panel, a second microservice for executing an NGS of the germline specimen to identify sequences of nucleotides in the germ line specimen using the targeted-panel to generate germ line sequencing results, a third microservice for executing an NGS of the somatic specimen to identify sequences of nucleotides in the somatic specimen using the targeted-panel to generate somatic sequencing results, a fourth microservice for executing quality control (QC) testing on the germ line sequencing results to generate a germ line QC score and on the somatic sequencing results to generate a somatic QC score, a fifth microservice for generating a clinical report, and a sixth microservice for providing the clinical report to the physician, the clinical report comprising the patient's TMB status.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
43.
Data based cancer research and treatment systems and methods
A method and system for conducting genomic sequencing, the method comprising storing a set of user application programs wherein each of the programs requires an application specific subset of data, for each of a plurality of patients that have cancerous cells and that receive cancer treatment, obtaining clinical records data in original forms including cancer state information, treatment types and treatment efficacy information, storing the clinical records data in a semi-structured first database, for each patient, using a genomic sequencer to generate genomic sequencing data for the patient's cancerous cells and normal cells, storing the sequencing data in the first database, shaping at least a subset of the first database data to generate system structured data including clinical record data and sequencing data wherein the system structured data is optimized for searching, storing the system structured data in a second database, for each user application program, selecting the application specific subset of data from the second database and storing the application specific subset of data in a structure optimized for application program interfacing in a third database.
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16B 50/30 - Data warehousingComputing architectures
G16H 20/00 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
44.
SYSTEMS AND METHODS FOR PREDICTING HOMOLOGOUS RECOMBINATION DEFICIENCY STATUS OF A SPECIMEN
Methods, systems, and software are provided for an ensemble model trained to distinguish between cancers with homologous recombination pathway deficiencies (HRD positive cancers) and cancers without homologous recombination pathway deficiencies (HRD negative cancers) based on nucleic acid sequencing data, e.g., both RNA and DNA sequencing data, generated from a cancerous tissue sample of the subject.
Techniques for analysis of gene expression data contained in real world data and real word evidence for assessing biologic pathways for identifying molecular subtypes are provided. Systems and methods include, for a plurality of biological pathways, determining a pathway score using gene expression data and determining of summary score for the plurality of biological pathways. That summary score may be compared to one or more enrichment scores each associated with a pre-determined molecular subtype. A molecular subtype is determined based on that comparison. Various heuristics may be applied to filter pathways before summary scoring. Additionally, techniques diagnose HER2 status for a patient, by identifying discordant HER2 status result between the HER2 status from immunohistochemistry (IHC) and the HER2 status from fluorescence in-situ hybridization (FISH) and diagnosing HER2 status based gene expression data.
G16B 25/10 - Gene or protein expression profilingExpression-ratio estimation or normalisation
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
46.
SYSTEMS AND METHODS FOR HIGH THROUGHPUT DRUG SCREENING
The disclosure provides a method of generating an artificial fluorescent image of a group of tumor organoids indicative of whether cells included in the group of tumor organoids are alive or dead. The method includes receiving a brightfield image, providing the brightfield image to a trained model, receiving the artificial fluorescent image from the trained model, and outputting the artificial fluorescent image to at least one of a memory or a display.
Methods, systems, and software are provided for using organoid cultures, e.g., patient-derived tumor organoid cultures, to improve treatment predictions and outcomes.
Systems and methods are provided for performing quality control analysis. The method obtains, in electronic form, a batch dataset comprising, for each respective sample in a batch of samples, a corresponding plurality of sequence reads derived from the respective sample by targeted or whole transcriptome RNA sequencing and corresponding metadata for the respective sample. The method determines for the batch dataset a cohort-matched reference batch, where the cohort- matched reference batch is balanced for tissue site, tumor purity, cancer type, sequencer identity, or date sequenced. The method performs one or more global batch quality control tests on the batch dataset using at least the cohort-matched reference batch. The method removes respective samples from the batch dataset that fail any one of the one or more global batch quality control tests or flagging for manual inspection respective samples that fail any one of the one or more global batch quality control tests.
G16B 20/40 - Population geneticsLinkage disequilibrium
G16B 30/00 - ICT specially adapted for sequence analysis involving nucleotides or amino acids
G16H 10/40 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
G16H 40/40 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management of medical equipment or devices, e.g. scheduling maintenance or upgrades
49.
TUMOR ORGANOID CULTURE COMPOSITIONS, SYSTEMS, AND METHODS
Provided herein are novel organoid culture media, organoid culture systems, and methods of culturing tumor organoids using the subject organoid culture media. Also provided herein are tumor organoids developed using such organoid culture systems, methods for assessing the clonal diversity of the tumor organoids, and methods for using such tumor organoids, for example, for tumor modelling and drug development applications. In particular embodiments, the tumor organoid culture media provided herein is substantially free of R-spondins (e.g., R- spondin 1).
Systems and methods are provided for implementing a tool for evaluating an effect on an event, such as a medication or treatment, on a subject's condition, using a propensity model that identifies matched treatment and control cohorts within a base population of subjects. A propensity value threshold, which can be obtained based on user input, can be used to adjust the selection of subjects for treatment and control cohorts. The tool allows analyzing features of the subjects in the treatment and control groups, and further allows for evaluation and comparison of survival objectives of subjects in the treatment and control groups.
G06F 15/16 - Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
G06Q 50/22 - Social work or social welfare, e.g. community support activities or counselling services
51.
Method and process for predicting and analyzing patient cohort response, progression, and survival
A system and method for analyzing a data store of de-identified patient data to generate one or more dynamic user interfaces usable to predict an expected response of a particular patient population or cohort when provided with a certain treatment. The automated analysis of patterns occurring in patient clinical, molecular, phenotypic, and response data, as facilitated by the various user interfaces, provides an efficient, intuitive way for clinicians to evaluate large data sets to aid in the potential discovery of insights of therapeutic significance.
G06Q 50/00 - Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
Methods for determining a cancer composition of a subject are provided that include generating machine-learning models configured to identify cell types based on respective cell-type RNA expression profiles, and using the models to determine the cancer composition of the subject.
Methods, systems, and software are provided for using organoid cultures, e.g., patient-derived tumor organoid cultures, to improve treatment predictions and outcomes.
C12Q 1/68 - Measuring or testing processes involving enzymes, nucleic acids or microorganismsCompositions thereforProcesses of preparing such compositions involving nucleic acids
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
A61B 5/00 - Measuring for diagnostic purposes Identification of persons
G01N 33/50 - Chemical analysis of biological material, e.g. blood, urineTesting involving biospecific ligand binding methodsImmunological testing
54.
Method and process for predicting and analyzing patient cohort response, progression, and survival
A system and method for analyzing a data store of de-identified patient data to generate one or more dynamic user interfaces usable to predict an expected response of a particular patient population or cohort when provided with a certain treatment. The automated analysis of patterns occurring in patient clinical, molecular, phenotypic, and response data, as facilitated by the various user interfaces, provides an efficient, intuitive way for clinicians to evaluate large data sets to aid in the potential discovery of insights of therapeutic significance.
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G06F 17/18 - Complex mathematical operations for evaluating statistical data
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 30/00 - ICT specially adapted for the handling or processing of medical images
G16H 80/00 - ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
G06F 18/22 - Matching criteria, e.g. proximity measures
G06F 18/214 - Generating training patternsBootstrap methods, e.g. bagging or boosting
G06F 18/2115 - Selection of the most significant subset of features by evaluating different subsets according to an optimisation criterion, e.g. class separability, forward selection or backward elimination
G06F 18/243 - Classification techniques relating to the number of classes
A method and system for predicting the likelihood that a patient will suffer from atrial fibrillation is provided. The method includes receiving electrocardiogram data associated with the patient, providing at least a portion of the electrocardiogram data to a trained model, receiving a risk score indicative of the likelihood the patient will suffer from atrial fibrillation within a predetermined period of time from when the electrocardiogram data was generated, and outputting the risk score to at least one of a memory or a display for viewing by a medical practitioner or healthcare administrator. The system includes at least one processor executing instructions to carry out the steps of the method.
A method and system for storing user application programs and micro-service programs, for each of multiple patients that have cancerous cells and receive treatment, includes obtaining clinical records data in original forms, storing it in a semi-structured first database, generating sequencing data for the patient's cancerous and normal cells using a next generation genomic sequencer, storing the sequencing data in the first database, shaping at least some of the first database data to generate system structured data optimized for searching and including clinical record data, storing the structured data in a second database, for each user application program, selecting an application-specific subset of data from the second database and storing it in a structure optimized for application program interfacing in a third database, wherein an orchestration manager operatively connected to one or more micro-service programs receives status messages and initiates a respective micro-service program when program prerequisites are satisfied.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 15/00 - ICT specially adapted for medical reports, e.g. generation or transmission thereof
G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
57.
SYSTEMS AND METHODS FOR DETECTING CELLULAR PATHWAY DYSREGULATION IN CANCER SPECIMENS
Disclosed herein are systems, methods, and compositions useful for determining cellular pathway disruption comprising the use of RNA expression level information. This determined level of disruption can assist in the identification of genetic variants that alter pathway activity, to correlate these variants with disease state and disease progression, and to identify those therapeutics most likely to be effective and which should be avoided.
C12N 15/10 - Processes for the isolation, preparation or purification of DNA or RNA
C12Q 1/6883 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
C12Q 1/6876 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
58.
UNSUPERVISED LEARNING AND PREDICTION OF LINES OF THERAPY FROM HIGH-DIMENSIONAL LONGITUDINAL MEDICATIONS DATA
In one aspect, the present disclosure provides a method for labeling one or more medications concurrently administered to a patient as a line of therapy. The method includes identifying medical records of the patient from a plurality of digital records, creating, from the subset of medical records, a plurality of treatment intervals including at least one medication administered to the patient and a time interval, associating medications of the one or more treatments with a respective treatment interval when the administration of the medication falls within the time interval, refining the time interval of a respective treatment interval when a treatment of the one or more treatments falls outside the time interval but within an extension period, identifying one or more potential lines of therapy from the plurality of treatment intervals, and labeling the potential line of therapy having the highest maximum likelihood estimation as the line of therapy.
G06F 15/18 - in which a program is changed according to experience gained by the computer itself during a complete run; Learning machines (adaptive control systems G05B 13/00;artificial intelligence G06N)
59.
Unsupervised learning and prediction of lines of therapy from high-dimensional longitudinal medications data
In one aspect, the present disclosure provides a method for labeling one or more medications concurrently administered to a patient as a line of therapy. The method includes identifying medical records of the patient from a plurality of digital records, creating, from the subset of medical records, a plurality of treatment intervals including at least one medication administered to the patient and a time interval, associating medications of the one or more treatments with a respective treatment interval when the administration of the medication falls within the time interval, refining the time interval of a respective treatment interval when a treatment of the one or more treatments falls outside the time interval but within an extension period, identifying one or more potential lines of therapy from the plurality of treatment intervals, and labeling the potential line of therapy having the highest maximum likelihood estimation as the line of therapy.
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G06K 9/00 - Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
60.
Systems and methods for detecting cellular pathway dysregulation in cancer specimens
Disclosed herein are systems, methods, and compositions useful for determining cellular pathway disruption comprising the use of RNA expression level information. This determined level of disruption can assist in the identification of genetic variants that alter pathway activity, to correlate these variants with disease state and disease progression, and to identify those therapeutics most likely to be effective and which should be avoided.
A system for personalized depression disorder treatment is disclosed herein. The system includes a server configured to communicate with existing healthcare resources and to receive patient data corresponding to a patient, the server including an analytics module. The system further includes a first database configured to store empirical patient outcomes, and further configured to communicate with the analytics module. Additionally, the system includes a user device having a graphical user interface (GUI) configured to communicate with the server and to display at least one output generated by the analytics module. The analytics module is configured to determine at least one of a personalized depression treatment and a personalized depression state prediction based on the empirical patient outcomes and the patient data.
C12Q 1/6883 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
Methods, systems, and software are provided for determining a microsatellite instability (MSI) status of a subject. Nucleotide sequences are obtained for cell-free DNA molecules from a liquid biopsy sample of the subject. The nucleotide sequences are used to determine, for each respective microsatellite locus in a plurality of predetermined microsatellite loci, one or more independent corresponding metrics, where each metric in the one or more metrics is determined at least in part by the distribution of the number of repeat units at the respective microsatellite locus. The one or more metrics are input into a classifier trained to distinguish between stable and unstable microsatellite loci, in order to classify the MSI status of the subject. In certain aspects, microsatellite stability metrics are compared against metrics from solid tumor samples and/or normal tissues. In certain aspects, the microsatellite stability metrics are determined relative to a subject-specific standard for microsatellite stability.
A genomic test processing system and method employ an order management engine and one or more order processing engines, the order processing engines including a receiving engine, an execution engine, and a broadcasting engine. The receiving engine receives a state of an order from the order management engine. The execution engine determines a sequence of steps to advance the received state of an order to a final state, iteratively designates each step of the sequence of steps as completed before initiating the next step of the sequence of steps, and advances the state of the order to a final state when a last step of the sequence of steps is completed. The broadcasting engine broadcasts the final state of the order to the order management engine. The order management engine causes one of the order processing engines to generate a next-generation sequencing report from the final state of the order.
C12Q 1/68 - Measuring or testing processes involving enzymes, nucleic acids or microorganismsCompositions thereforProcesses of preparing such compositions involving nucleic acids
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
G06F 17/18 - Complex mathematical operations for evaluating statistical data
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
Systems and methods are configured to match a patient to a clinical trial. A method includes receiving text-based criteria for the clinical trial, including a molecular marker. Additionally, the method includes associating at least a portion of the text-based criteria to one or more pre-defined data fields containing molecular marker information. The method further includes comparing a molecular marker of the patient to the one or more pre-defmed data fields, and generating a report for a provider. The report is based on the comparison and includes a match indication of the patient to the clinical trial.
G06K 9/00 - Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
G06K 9/62 - Methods or arrangements for recognition using electronic means
G06K 9/64 - Methods or arrangements for recognition using electronic means using simultaneous comparisons or correlations of the image signals with a plurality of references, e.g. resistor matrix
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
66.
SYSTEMS AND METHODS FOR MULTI-LABEL CANCER CLASSIFICATION
Systems and methods are provided for identifying a diagnosis of a cancer condition for a somatic tumor specimen of a subject. The method receives sequencing information comprising analysis of a plurality of nucleic acids derived from the somatic tumor specimen. The method identifies a plurality of features from the sequencing information, including two or more of RNA, DNA, RNA splicing, viral, and copy number features. The method provides a first subset of features and a second subset of features from the identified plurality of features as inputs to a first classifier and a second classifier, respectively. The method generates, from two or more classifiers, two or more predictions of cancer condition based at least in part on the identified plurality of features. The method combines, at a final classifier, the two or more predictions to identify the diagnosis of the cancer condition for the somatic tumor specimen of the subject.
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
G16H 20/40 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
G16H 20/10 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
67.
COLLABORATIVE ARTIFICIAL INTELLIGENCE METHOD AND SYSTEM
A method and system of audibly broadcasting responses to a user based on user queries about a specific patient molecular report, the method comprising receiving an audible query from the user to a microphone coupled to a collaboration device, identifying at least one intent associated with the audible query, identifying at least one data operation associated with the at least one intent, associating each of the at least one data operations with a first set of data presented on the molecular report, executing each of the at least one data operations on a second set of data to generate response data, generating an audible response file associated with the response data and providing the audible response file for broadcasting via a speaker coupled to the collaboration device.
G01N 33/00 - Investigating or analysing materials by specific methods not covered by groups
G06F 19/00 - Digital computing or data processing equipment or methods, specially adapted for specific applications (specially adapted for specific functions G06F 17/00;data processing systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes G06Q;healthcare informatics G16H)
G10L 21/00 - Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
A method and system of audibly broadcasting responses to a user based on user queries about a specific patient molecular report, the method comprising receiving an audible query from the user to a microphone coupled to a collaboration device, identifying at least one intent associated with the audible query, identifying at least one data operation associated with the at least one intent, associating each of the at least one data operations with a first set of data presented on the molecular report, executing each of the at least one data operations on a second set of data to generate response data, generating an audible response file associated with the response data and providing the audible response file for broadcasting via a speaker coupled to the collaboration device.
G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 70/60 - ICT specially adapted for the handling or processing of medical references relating to pathologies
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
G16H 70/20 - ICT specially adapted for the handling or processing of medical references relating to practices or guidelines
G06F 3/0481 - Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
A method and system of audibly broadcasting responses to a user based on user queries about a specific patient report, the method comprising receiving an audible query from the user to a microphone coupled to a collaboration device, identifying at least one intent associated with the audible query, identifying at least one data operation associated with the at least one intent, associating each of the at least one data operations with a first set of data presented on the report, executing each of the at least one data operations on a second set of data to generate response data, generating an audible response file associated with the response data and providing the audible response file for broadcasting via a speaker coupled to the collaboration device.
G10L 15/22 - Procedures used during a speech recognition process, e.g. man-machine dialog
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 70/60 - ICT specially adapted for the handling or processing of medical references relating to pathologies
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
G16H 40/20 - ICT specially adapted for the management or administration of healthcare resources or facilitiesICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
G16H 70/20 - ICT specially adapted for the handling or processing of medical references relating to practices or guidelines
G06F 3/0481 - Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
A computer program product includes multiple microservices for interrogating clinical records according to one or more projects associated with patient datasets obtained from electronic copies of source documents from the clinical records. A first microservice generates a user interface including a first portion displaying source documents and, concurrently, a second portion displaying structured patient data fields organized into categories for entering structured patient data derived from the source documents displayed in the first portion. Categories and their organization are defined by a template and include cancer diagnosis, staging, tumor size, genetic results, and date of recurrence. A second microservice validates abstracted patient data according to validation rules applied to the categories, validation rules being assigned to the projects and performed on the categories as they are populated. A third microservice provides abstraction review performed by an assigned abstractor or an abstraction manager and spans one or more of the projects.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G06Q 50/22 - Social work or social welfare, e.g. community support activities or counselling services
71.
DETERMINING BIOMARKERS FROM HISTOPATHOLOGY SLIDE IMAGES
A generalizable and interpretable deep learning model for predicting biomarker status and biomarker metrics from histopathology slide images is provided.
G06F 19/00 - Digital computing or data processing equipment or methods, specially adapted for specific applications (specially adapted for specific functions G06F 17/00;data processing systems or methods specially adapted for administrative, commercial, financial, managerial, supervisory or forecasting purposes G06Q;healthcare informatics G16H)
72.
DETECTION OF HUMAN LEUKOCYTE ANTIGEN LOSS OF HETEROZYGOSITY
Processes are provided for detecting loss of heterozygosity of Human Leukocyte Antigen (HLA) in a subject using analysis of next generation sequencing (NGS) data. The processes include aligning NGS data and identifying unmapped and mapped reads, updating reference data, and feeding one or more sequence reads to an HLA typing process for identifying candidate HLA alleles and feeding HLA type data to a loss of heterozygosity (LOH) modeling process for determining a LOH status for each HLA allele. A report may be generated of the LOH statuses for each of HLA allele.
A platform for transcriptome deconvolution of gene expression data is provided and may be used in assessing metastatic cancer samples. The deconvolution is performed using an unsupervised clustering technique, such as grade of membership, that allows for samples to be assigned to multiple clusters during a training process. A deconvolution gene expression model is generated as a result and is used for accurate assess of metastases in subsequent samples.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
74.
A METHOD AND PROCESS FOR PREDICTING AND ANALYZING PATIENT COHORT RESPONSE, PROGRESSION, AND SURVIVAL
A system and method for analyzing a data store of de-identified patient data to generate one or more dynamic user interfaces usable to predict an expected response of a particular patient population or cohort when provided with a certain treatment. The automated analysis of patterns occurring in patient clinical, molecular, phenotypic, and response data, as facilitated by the various user interfaces, provides an efficient, intuitive way for clinicians to evaluate large data sets to aid in the potential discovery of insights of therapeutic significance.
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
G16H 50/80 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for detecting, monitoring or modelling epidemics or pandemics, e.g. flu
75.
AUTOMATED QUALITY ASSURANCE TESTING OF STRUCTURED CLINICAL DATA
A method for checking the quality of data content in a structured clinical record is disclosed. The method may include the steps of providing a data quality test that checks the content of at least a portion of the data content in the structured clinical record, applying the data quality test to the portion of the data content, and returning the results of the data quality test.
Techniques for generating an overlay map on a digital medical image of a slide are provided, and include cell detection and tissue classification processes. Techniques include receiving a medical image, separating the image into tiles, and performing tile classifications and tissue classifications based on a multi-tile analysis. Techniques additionally include identifying cell objects in the image, separating the image into and displaying polygons identifying the cell objects and cell classifications. Generated displays may be overlays over the initial digital image.
A method includes the steps of determining a first concept from a text of a medical record from an electronic health record system, the first concept relating to a patient, identifying a match to the first concept in a first list of concepts, wherein the first list of concepts is not a predetermined authority, referencing the first concept with an entity in a database of related concepts, identifying a match to a second concept in a second list of concepts, the second list of concepts not directly linked to the first list of concepts except by a relationship to the entity, wherein the second list of concepts is the predetermined authority, and providing the second concept as an identifier of the patient's medical record.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
G16H 10/00 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data
78.
USER INTERFACE, SYSTEM, AND METHOD FOR COHORT ANALYSIS
A system and method that receive a distance matrix for multiple patients and a patient of interest, assign a radial distance value between the patient of interest and the other patients based on the distance matrix value for each of the multiple patients, generate an angular distance value between the multiple patients based at least in part on a measure of similarity between each patient, and minimize a cost function based at least in part on the angular distance value between each patient and each other patient. Minimizing the cost function may include calculating a patient contribution to the cost function for a plurality of angular distance values and selecting the angular distance value with the smallest patient contribution. The processor also may be configured to generate and display a radar plot based on the assigned radial distance value and generated angular distance value of each patient.
A system and method that receive a distance matrix for multiple patients and a patient of interest, assign a radial distance value between the patient of interest and the other patients based on the distance matrix value for each of the multiple patients, generate an angular distance value between the multiple patients based at least in part on a measure of similarity between each patient, and minimize a cost function based at least in part on the angular distance value between each patient and each other patient. Minimizing the cost function may include calculating a patient contribution to the cost function for a plurality of angular distance values and selecting the angular distance value with the smallest patient contribution. The processor also may be configured to generate and display a radar plot based on the assigned radial distance value and generated angular distance value of each patient.
G06K 9/62 - Methods or arrangements for recognition using electronic means
G16B 45/00 - ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks
G16B 25/10 - Gene or protein expression profilingExpression-ratio estimation or normalisation
G16H 20/00 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
80.
MICROSATELLITE INSTABILITY DETERMINATION SYSTEM AND RELATED METHODS
Methods and systems for determining microsatellite instability (MSI) directly from microsatellite region mappings for specific loci in the genome are provided. Techniques include an MSI assay that may be deployed in a paired form, that is, as tumor sample and matched normal sample MSI assay, or an unpaired form, that is, as a tumor-only MSI assay. The techniques provide an automated process for MSI determination by mapping read counts in tumor samples and normal samples and comparing the two, for an identified set of 43 microsatellite loci.
C12Q 1/68 - Measuring or testing processes involving enzymes, nucleic acids or microorganismsCompositions thereforProcesses of preparing such compositions involving nucleic acids
C07K 16/30 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants from tumour cells
C07K 16/40 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against enzymes
81.
DATA BASED CANCER RESEARCH AND TREATMENT SYSTEMS AND METHODS
A method and system for conducting genomic sequencing, the method comprising storing a set of user application programs wherein each of the programs requires an application specific subset of data, for each of a plurality of patients that have cancerous cells and that receive cancer treatment, obtaining clinical records data in original forms including cancer state information, treatment types and treatment efficacy information, storing the clinical records data in a semi-structured first database, for each patient, using a genomic sequencer to generate genomic sequencing data for the patient's cancerous cells and normal cells, storing the sequencing data in the first database, shaping at least a subset of the first database data to generate system structured data including clinical record data and sequencing data wherein the system structured data is optimized for searching, storing the system structured data in a second database, for each user application program, selecting the application specific subset of data from the second database and storing the application specific subset of data in a structure optimized for application program interfacing in a third database.
G16H 10/60 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
G16H 50/20 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
G16H 50/30 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indicesICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for individual health risk assessment
G16H 50/50 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
G16H 50/70 - ICT specially adapted for medical diagnosis, medical simulation or medical data miningICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
82.
METHODS OF NORMALIZING AND CORRECTING RNA EXPRESSION DATA
A platform to perform normalization and correction on gene expression datasets and combines different datasets into a standard dataset using a framework configured to continuously incorporate new gene expression data. The framework determines a series of conversion factors that are used to on-board new gene expression datasets, such as unpaired datasets, where these conversion factors are able to correct for variations in data type, variations in gene expressions, and variations in collection systems.
C12Q 1/6886 - Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
G06F 19/20 - for hybridisation or gene expression, e.g. microarrays, sequencing by hybridisation, normalisation, profiling, noise correction models, expression ratio estimation, probe design or probe optimisation
G06F 19/22 - for sequence comparison involving nucleotides or amino acids, e.g. homology search, motif or Single-Nucleotide Polymorphism [SNP] discovery or sequence alignment
83.
A MULTI-MODAL APPROACH TO PREDICTING IMMUNE INFILTRATION BASED ON INTEGRATED RNA EXPRESSION AND IMAGING FEATURES
Multi-modal approaches to predict tumor immune infiltration are based on integrating gene expression data and imaging features in a neural network-based framework. This framework is configured to estimate percent composition, and thus immune infiltration score, of a patient tumor biopsy sample. Multi-modal approaches may also be used to predict cell composition beyond immune cells via integrated multi-layer neural network frameworks.
G16B 40/00 - ICT specially adapted for biostatisticsICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
84.
3D RADIOMIC PLATFORM FOR IMAGING BIOMARKER DEVELOPMENT
A platform is provided for generating 3D models of a tumor segmented from a series of 2D medical images and for identifying from these 3D models, radiomic features that may be used for diagnostic, prognostic, and treatment response assessment of the tumor. The radiomic features may be shape-based features, intensity-based features, textural features, and filter-based features. The radiomic features are compared to remove sufficiently redundant features, thereby producing a reduced set of radiomic features, which is then compared to separate genomic data and/or outcome data to identify clinically and biologically significant radiomic features for diagnostic, prognostic, and treatment response assessment, other applications.
A generalizable and interpretable deep learning model for predicting microsatellite instability from histopathology slide images is provided. Microsatellite instability (MSI) is an important genomic phenotype that can direct clinical treatment decisions, especially in the context of cancer immunotherapies. A deep learning framework is provided to predict MSI from histopathology images, to improve the generalizability of the predictive model using adversarial training to new domains, such as on new data sources or tumor types, and to provide techniques to visually interpret the topological and morphological features that influence the MSI predictions.
