Displaying a comparison of genotypic information between relatives is disclosed, including receiving an indication that a first individual is a grandparent, receiving an indication that a second individual is a grandchild of the first individual, comparing the genotypic information of the first individual and the second individual and calculating a similarity score, and displaying an indication of the similarity score graphically using colors.
The disclosed embodiments concern methods, apparatus, systems, and computer program products for storing and retrieving genetic data for individuals. In some implementations, a storage format is provided that allows genetic data to be defined by metadata for reproduce-ability.
G16B 50/30 - Data warehousingComputing architectures
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
G16B 50/00 - ICT programming tools or database systems specially adapted for bioinformatics
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
The disclosed embodiments concern methods, apparatus, systems, and computer program products for developing polygenic risk score (PRS) models. In some implementations, a fully automated process is provided that allows for a PRS model to be defined by an initial set of parameters. In some implementations the PRS models are trained to provide a PRS for particular populations.
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
Displaying an indication of ancestral data is disclosed. An indication that a genetic interval corresponds to a reference interval that has a likelihood of having one or more ancestral origins is received. One or more graphic display parameters are determined based at least in part on the indication. An indication of the one or more ancestral origins is visually displayed using the one or more graphic display parameters.
G06F 3/04812 - Interaction techniques based on cursor appearance or behaviour, e.g. being affected by the presence of displayed objects
G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
G06F 11/07 - Responding to the occurrence of a fault, e.g. fault tolerance
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
G16B 20/40 - Population geneticsLinkage disequilibrium
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
G16B 45/00 - ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks
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
An embodiment may involve storing, by a computing device and in a database, a set of pangenetic attributes of a set of individuals, wherein the pangenetic attributes of the set are respectively and statistically associated with products; based on the statistical associations between the pangenetic attributes and the products, determining, by the computing device, product recommendations for a second set of individuals; receiving, by the computing device and from the second set of individuals, a plurality of measures of satisfaction with the product recommendations; based on the plurality of measures of satisfaction, learning, by the computing device, an association between a subset of the pangenetic attributes and a particular product; and storing, by the computing device and in the database, the learned association, wherein the learned association provides a basis for subsequent recommendations of the particular product when a subsequent individual exhibits the subset of the pangenetic attributes.
Ancestry deconvolution includes obtaining unphased genotype data of an individual; phasing, using one or more processors, the unphased genotype data to generate phased haplotype data; using a learning machine to classify portions of the phased haplotype data as corresponding to specific ancestries respectively and generate initial classification results; and correcting errors in the initial classification results to generate modified classification results.
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
Example embodiments relate to identity-by-descent (IBD) relatedness based on focal and reference segments. An example method includes determining, by a services platform based on personal information of a focal individual, a focal string. The method also includes retrieving, by the services platform from a reference database, a reference string of a reference individual. Additionally, the method includes computationally identifying, by the services platform, IBD segments between the focal string and the reference string. Further, the method includes determining, by the services platform and based on the merged set of IBD segments, a degree of relatedness between the focal individual and the reference individual. In addition, the method includes providing, by the services platform, access to the degree of relatedness via a user interface.
Databases and data processing systems for use with a network-based personal genetics services platform may include member information pertaining to a plurality of members of the network-based personal genetics services platform. The member information may include genetic information, family history information, environmental information, and phenotype information of the plurality of members. A data processing system may determine, based at least in part on the member information, a model for predicting a phenotype from genetic information, family history information, and environmental information, wherein determining the model includes training the model using the member information pertaining to a set of the plurality of members. The data processing system may also receive a request from a questing member to predict a phenotype of interest, and apply an individual's genetic information, family history information, and environmental information to the model to obtain a prediction associated with the phenotype of interest for the requesting member.
G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
G16B 50/00 - ICT programming tools or database systems specially adapted for bioinformatics
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 50/30 - 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
Inferring a characteristic of an individual is disclosed. An indication that a first user and a second user have at least one shared chromosomal segment is received. Information about the second user is obtained. A characteristic of the first user is inferred based at least in part on the information about the second user.
A method, software, database and system for attribute partner identification and social network based attribute analysis are presented in which attribute profiles associated with individuals can be compared and potential partners identified. Connections can be formed within social networks based on analysis of genetic and non-genetic data. Degrees of attribute separation (genetic and non-genetic) can be utilized to analyze relationships and to identify individuals who might benefit from being connected.
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
G16B 20/40 - Population geneticsLinkage disequilibrium
G16H 20/30 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
G16H 40/63 - 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 operation of medical equipment or devices for local operation
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 50/70 - 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
11.
DETERMINING FAMILY CONNECTIONS OF INDIVIDUALS IN A DATABASE
Determining relative connections between individuals includes: obtaining identification information of a first individual and identification information of a second individual; determining, based at least in part on a relative connections graph, a relative connections path connecting the first individual, the second individual, and at least one additional individual; and outputting information pertaining to the relative connections path.
G16B 10/00 - ICT specially adapted for evolutionary bioinformatics, e.g. phylogenetic tree construction or analysis
G16B 99/00 - Subject matter not provided for in other groups of this subclass
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
G16Z 99/00 - Subject matter not provided for in other main groups of this subclass
12.
Genetic Determination of Predispositions for Health-Related Conditions
A method, software, database, and system in which a query attribute is used as the basis for accessing stored attribute combinations and their frequencies of occurrence for individuals; and tabulating, based on frequencies of occurrence, those attribute combinations that are most likely to co-occur with the query attribute.
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
G16B 20/40 - Population geneticsLinkage disequilibrium
G16H 20/30 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
G16H 40/63 - 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 operation of medical equipment or devices for local operation
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 50/70 - 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
The present disclosure relates to a ULBP6 binding protein that inhibits the interaction between ULBP6 and NKG2D, and methods of treating cancer with said ULBP6 binding protein.
C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
Provided herein are transgenic mice producing the common light chain antibodies comprising IGKV10-96 and IGKJ1, or a variant thereof, libraries of B cells or antibodies arising from such mice, methods for producing libraries of B cells or antibodies arising from such mice, and related recombinant gene segments.
C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
C12N 15/79 - Vectors or expression systems specially adapted for eukaryotic hosts
1. Presenting ancestral origin information, comprising: receiving a request to display ancestry data of an individual; obtaining ancestry composition information of the individual, the ancestry composition information including information pertaining to a proportion of the individual's genotype data that is deemed to correspond to a specific ancestry; and presenting the ancestry composition information to be displayed.
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
Processing genetic information comprises: receiving an input that includes information pertaining to a specific genetic variant; and identifying, in a database comprising genotype information of a plurality of candidate individuals, a matching individual imputed to have the specific genetic variant. The genotype information of the matching individual corresponding to the specific genetic variant is not directly assayed.
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
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
G16B 50/00 - ICT programming tools or database systems specially adapted for bioinformatics
G16B 50/30 - Data warehousingComputing architectures
Assembling a cohort includes: receiving genetic characteristic information pertaining to a desired genetic characteristic; using the genetic characteristic information to search a data storage comprising information of previously genotyped individuals to derive a candidate group having the desired genetic characteristic; and assembling the cohort based at least in part on the candidate group.
G06F 16/9535 - Search customisation based on user profiles and personalisation
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
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 50/00 - ICT programming tools or database systems specially adapted for bioinformatics
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
18.
Methods and Systems for Determining and Displaying Pedigrees
The disclosed embodiments concern methods, apparatus, systems and computer program products for determining and displaying pedigrees based on IBD data. Some implementations use a probabilistic relationship model to obtain various likelihoods of various potential relationships based on pairwise IBD data and pairwise age data. Some implementations build large pedigrees by combining smaller pedigrees. Some implementations display pedigree graphs with various features that are informative and easy to understand.
G06T 11/20 - Drawing from basic elements, e.g. lines or circles
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
G06F 3/04842 - Selection of displayed objects or displayed text elements
Example embodiments relate to analyzing and merging data from genome-wide association studies. An example embodiment includes a method. The method includes receiving, by a processor from a memory, a candidate data set including data from a genetic study conducted within a population. The data from the genetic study includes a plurality of gene variants determined within the population. The method also includes removing one or more of the plurality of gene variants from the candidate data set in order to generate a revised candidate data set based on one or more variant-level quality metrics. Further, the method includes determining whether the revised candidate data set satisfies one or more study-level quality metrics. Additionally, the method includes establishing data set metadata based on whether the revised candidate data set satisfies one or more study-level quality metrics. Further, the method includes storing, within the memory, the data set metadata.
Example embodiments relate to analyzing and merging data from genome-wide association studies. An example embodiment includes a method. The method includes receiving, by a processor from a memory, a candidate data set including data from a genetic study conducted within a population. The data from the genetic study includes a plurality of gene variants determined within the population. The method also includes removing one or more of the plurality of gene variants from the candidate data set in order to generate a revised candidate data set based on one or more variant-level quality metrics. Further, the method includes determining whether the revised candidate data set satisfies one or more study-level quality metrics. Additionally, the method includes establishing data set metadata based on whether the revised candidate data set satisfies one or more study-level quality metrics. Further, the method includes storing, within the memory, the data set metadata.
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
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 25/10 - Gene or protein expression profilingExpression-ratio estimation or normalisation
Sharing data is disclosed. In some cases, sharing data includes receiving a request to share data from a first account to a second account, receiving an indication of a plurality of first account profiles associated with the first account to share with the second account, and establishing sharing from the plurality of first account profiles to the second account, wherein sharing comprises the second account having read access to a subset of nonpublic data associated with the plurality of first account profiles.
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
G06F 16/9535 - Search customisation based on user profiles and personalisation
G06F 16/958 - Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking
G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
The present disclosure relates to an inhibitor of human TRPM3 for use in the treatment of epilepsy wherein the inhibitor of human TRPM3 is selective for human TRPM3 over human GABAA receptors.
A61K 31/4015 - Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins having five-membered rings with one nitrogen as the only ring hetero atom, e.g. sulpiride, succinimide, tolmetin, buflomedil having oxo groups directly attached to the heterocyclic ring, e.g. piracetam, ethosuximide
A61K 31/53 - Heterocyclic compounds having nitrogen as a ring hetero atom, e.g. guanethidine or rifamycins having six-membered rings with three nitrogens as the only ring hetero atoms, e.g. chlorazanil, melamine
A61K 45/06 - Mixtures of active ingredients without chemical characterisation, e.g. antiphlogistics and cardiaca
A61P 25/12 - AntiepilepticsAnticonvulsants for grand-mal
1. Determining relative relationships of people who share a common ancestor within at least a threshold number of generations includes: receiving recombinable deoxyribonucleic acid (DNA) sequence information of a first user and recombinable DNA sequence information of a plurality of users; processing, using one or more computer processors, the recombinable DNA sequence information of the plurality of users in parallel; determining, based at least in part on a result of processing the recombinable DNA information of the plurality of users in parallel, a predicted degree of relationship between the first user and a user among the plurality of users, the predicted degree of relative relationship corresponding to a number of generations within which the first user and the second user share a common ancestor.
An embodiment may involve storing, by a computing device and in a database, a set of pangenetic attributes of a set of individuals, wherein the pangenetic attributes of the set are respectively and statistically associated with products; based on the statistical associations between the pangenetic attributes and the products, determining, by the computing device, product recommendations for a second set of individuals; receiving, by the computing device and from the second set of individuals, a plurality of measures of satisfaction with the product recommendations; based on the plurality of measures of satisfaction, learning, by the computing device, an association between a subset of the pangenetic attributes and a particular product; and storing, by the computing device and in the database, the learned association, wherein the learned association provides a basis for subsequent recommendations of the particular product when a subsequent individual exhibits the subset of the pangenetic attributes.
Example embodiments relate to identity-by-descent (IBD) relatedness based on focal and reference segments. An example method includes determining, by a services platform based on personal information of a focal individual, a focal string. The method also includes retrieving, by the services platform from a reference database, a reference string of a reference individual. Additionally, the method includes computationally identifying, by the services platform, IBD segments between the focal string and the reference string. Further, the method includes determining, by the services platform and based on the merged set of IBD segments, a degree of relatedness between the focal individual and the reference individual. In addition, the method includes providing, by the services platform, access to the degree of relatedness via a user interface.
Displaying an indication of ancestral data is disclosed. An indication that a genetic interval corresponds to a reference interval that has a likelihood of having one or more ancestral origins is received. One or more graphic display parameters are determined based at least in part on the indication. An indication of the one or more ancestral origins is visually displayed using the one or more graphic display parameters.
G06F 3/048 - Interaction techniques based on graphical user interfaces [GUI]
G06F 3/04812 - Interaction techniques based on cursor appearance or behaviour, e.g. being affected by the presence of displayed objects
G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
G06F 11/07 - Responding to the occurrence of a fault, e.g. fault tolerance
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
G16B 20/40 - Population geneticsLinkage disequilibrium
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
G16B 45/00 - ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks
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
27.
Identity-by-descent relatedness based on focal and reference segments
Example embodiments relate to identity-by-descent (IBD) relatedness based on focal and reference segments. An example method includes determining, by a services platform based on personal information of a focal individual, a focal string. The method also includes retrieving, by the services platform from a reference database, a reference string of a reference individual. Additionally, the method includes computationally identifying, by the services platform, IBD segments between the focal string and the reference string. Further, the method includes determining, by the services platform and based on the merged set of IBD segments, a degree of relatedness between the focal individual and the reference individual. In addition, the method includes providing, by the services platform, access to the degree of relatedness via a user interface.
Inferring a characteristic of an individual is disclosed. An indication that a first user and a second user have at least one shared chromosomal segment is received. Information about the second user is obtained. A characteristic of the first user is inferred based at least in part on the information about the second user.
Determining relative relationships of people who share a common ancestor within at least a threshold number of generations includes: receiving recombinable deoxyribonucleic acid (DNA) sequence information of a first user and recombinable DNA sequence information of a plurality of users; processing, using one or more computer processors, the recombinable DNA sequence information of the plurality of users in parallel; determining, based at least in part on a result of processing the recombinable DNA information of the plurality of users in parallel, a predicted degree of relationship between the first user and a user among the plurality of users, the predicted degree of relative relationship corresponding to a number of generations within which the first user and the second user share a common ancestor.
09 - Scientific and electric apparatus and instruments
Goods & Services
Kits for use in genetic identity testing for scientific and
research purposes comprising a saliva collection tube, caps
for tube for use in DNA testing of humans.
The present disclosure relates to an inhibitor of human TRPM3 for use in the treatment or prevention of migraine. Combination therapies are also described. In other aspects, the present disclosure provides methods for identifying suitable patients, methods for identifying inhibitors of human TRPM3 and cell lines for use in such methods.
A61K 31/353 - 3,4-Dihydrobenzopyrans, e.g. chroman, catechin
A61K 31/4045 - Indole-alkylaminesAmides thereof, e.g. serotonin, melatonin
A61K 31/573 - Compounds containing cyclopenta[a]hydrophenanthrene ring systemsDerivatives thereof, e.g. steroids substituted in position 17 beta by a chain of two carbon atoms, e.g. pregnane or progesterone substituted in position 21, e.g. cortisone, dexamethasone, prednisone or aldosterone
A61K 38/48 - Hydrolases (3) acting on peptide bonds (3.4)
A61K 45/06 - Mixtures of active ingredients without chemical characterisation, e.g. antiphlogistics and cardiaca
The present disclosure provides binding proteins, such as antibodies and antigen-binding fragments, which specifically bind to human IL-36 cytokines, IL-36α, IL-36β, and/or IL-36γ, and block the IL-36 stimulated signaling pathways. Compositions comprising such binding proteins and methods of making and using such binding proteins are also provided.
C07K 16/24 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against cytokines, lymphokines or interferons
A61P 29/00 - Non-central analgesic, antipyretic or antiinflammatory agents, e.g. antirheumatic agentsNon-steroidal antiinflammatory drugs [NSAID]
The present disclosure relates to FLT3LG binding proteins that inhibit the interaction between FLT3LG and FLT3, and methods of treating autoimmune diseases with said FLT3LG binding proteins.
An example embodiment may involve estimating, from genetic data of a plurality of individuals, identity-by-descent (IBD) segments; forming, from the IBD segments, a relationship graph representing genetic linkages between the individuals; determining, by applying a stochastic block model to the relationship graph, a plurality of genetic groups, wherein each of the genetic groups is assigned a respective subset of the individuals who share a greater amount of IBD segment length with one another than with a further respective subset of the individuals who are in other of the genetic groups; and training, for each of the genetic groups, a respective classifier based on (i) input including genome-wide local ancestry proportions of the individuals and sums of IBD segments for the individuals in the respective genetic group, and (ii) associated output of assignments of the individuals to the genetic groups.
An example embodiment may involve estimating, from genetic data of a plurality of individuals, identity-by-descent (IBD) segments; forming, from the IBD segments, a relationship graph representing genetic linkages between the individuals: determining, by applying a stochastic block model to the relationship graph, a plurality of genetic groups, wherein each of the genetic groups is assigned a respective subset of the individuals who share a greater amount of IBD segment length with one another than with a further respective subset of the individuals who are in other of the genetic groups; and training, for each of the genetic groups, a respective classifier based on (i) input including genome-wide local ancestry proportions of the individuals and sums of IBD segments for the individuals in the respective genetic group, and (ii) associated output of assignments of the individuals to the genetic groups.
G16B 20/40 - Population geneticsLinkage disequilibrium
G16B 45/00 - ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks
G16B 5/00 - ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
36.
Window-Based Method for Determining Inherited Segments
Displaying a comparison of genetic data is disclosed, including receiving an indication of a first individual, receiving an indication of a second individual, retrieving the genotypic information for the first individual and the second individual, comparing the genotypic information of the first individual and the second individual, displaying an indication of the comparison of the genotypic information of the first individual and the second individual graphically. A first graphical symbol is used to display an indication of the genome regions for which the first individual and the second individual are identical. A second graphical symbol is used to display an indication of the genome regions for which the first individual and the second individual are half identical.
42 - Scientific, technological and industrial services, research and design
Goods & Services
Providing scientific analysis and informational reports based upon results of laboratory testing in the field of genetics; providing online non-downloadable software allowing website users to generate information and conduct analyses of their test results based upon the results of genetic testing among historical individuals
Presenting ancestral origin information, comprising: receiving a request to display ancestry data of an individual; obtaining ancestry composition information of the individual, the ancestry composition information including information pertaining to a proportion of the individual's genotype data that is deemed to correspond to a specific ancestry; and presenting the ancestry composition information to be displayed.
G16B 10/00 - ICT specially adapted for evolutionary bioinformatics, e.g. phylogenetic tree construction or analysis
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
Displaying an indication of ancestral data is disclosed. An indication that a genetic interval corresponds to a reference interval that has a likelihood of having one or more ancestral origins is received. One or more graphic display parameters are determined based at least in part on the indication. An indication of the one or more ancestral origins is visually displayed using the one or more graphic display parameters.
G06F 3/048 - Interaction techniques based on graphical user interfaces [GUI]
G06F 3/04812 - Interaction techniques based on cursor appearance or behaviour, e.g. being affected by the presence of displayed objects
G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
G06F 11/07 - Responding to the occurrence of a fault, e.g. fault tolerance
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
G16B 20/40 - Population geneticsLinkage disequilibrium
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
G16B 45/00 - ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks
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
40.
Anti-CD200R1 antibodies and methods of use thereof
The present disclosure provides binding proteins, such as antibodies and antigen-binding fragments, which specifically bind to human CD200R1 receptor protein (hu-CD200R1) and are capable of decreasing, inhibiting, and/or fully-blocking immune regulatory effects mediated by hu-CD200R1. The present disclosure also provides methods of using the antibodies (and compositions thereof) to treat diseases and conditions responsive to decreasing, inhibiting and/or blocking immune regulatory function or activity mediated by CD200 binding to CD200R1.
C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
A61K 39/00 - Medicinal preparations containing antigens or antibodies
41.
Window-based method for determining inherited segments
Displaying a comparison of genetic data is disclosed, including receiving an indication of a first individual, receiving an indication of a second individual, retrieving the genotypic information for the first individual and the second individual, comparing the genotypic information of the first individual and the second individual, displaying an indication of the comparison of the genotypic information of the first individual and the second individual graphically. A first graphical symbol is used to display an indication of the genome regions for which the first individual and the second individual are identical. A second graphical symbol is used to display an indication of the genome regions for which the first individual and the second individual are half identical.
A method, software, database, and system in which a query attribute is used as the basis for accessing stored attribute combinations and their frequencies of occurrence for individuals; and tabulating, based on frequencies of occurrence, those attribute combinations that are most likely to co-occur with the query attribute.
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
G16B 20/40 - Population geneticsLinkage disequilibrium
G16H 20/30 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
G16H 40/63 - 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 operation of medical equipment or devices for local operation
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 50/70 - 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 70/20 - ICT specially adapted for the handling or processing of medical references relating to practices or guidelines
The disclosed embodiments concern methods, apparatus, systems, and computer program products for storing and retrieving genetic data for individuals. In some implementations, a storage format is provided that allows genetic data to be defined by metadata for reproduce-ability.
G16B 50/30 - Data warehousingComputing architectures
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
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
G16B 50/00 - ICT programming tools or database systems specially adapted for bioinformatics
Error correction in ancestry classification includes obtaining, from a classifier, initial ancestry classifications associated with portions of two phased haplotypes of a chromosome pair of an individual; performing error correction on an initial ancestry classification, including detecting a phasing error in the initial ancestry classifications; and outputting a corrected ancestry classification in which the phasing error is corrected.
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
Assembling a cohort includes: receiving genetic characteristic information pertaining to a desired genetic characteristic; using the genetic characteristic information to search a data storage comprising information of previously genotyped individuals to derive a candidate group having the desired genetic characteristic; and assembling the cohort based at least in part on the candidate group.
The present disclosure relates to a ULBP6 binding protein that inhibits the interaction between ULBP6 and NKG2D, and methods of treating cancer with said ULBP6 binding protein.
C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
The present disclosure relates to a ULBP6 binding protein that inhibits the interaction between ULBP6 and NKG2D, and methods of treating cancer with said ULBP6 binding protein.
A method, software, database and system for attribute partner identification and social network based attribute analysis are presented in which attribute profiles associated with individuals can be compared and potential partners identified. Connections can be formed within social networks based on analysis of genetic and non-genetic data. Degrees of attribute separation (genetic and non-genetic) can be utilized to analyze relationships and to identify individuals who might benefit from being connected.
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
G16B 20/40 - Population geneticsLinkage disequilibrium
G16H 20/30 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
G16H 40/63 - 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 operation of medical equipment or devices for local operation
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 50/70 - 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 70/20 - ICT specially adapted for the handling or processing of medical references relating to practices or guidelines
49.
POLYGENIC RISK STRATIFICATION METHODS FOR TYPE 2 DIABETES
The present disclosure relates to methods employing polygenic scores for determining and stratifying risk of development of type 2 diabetes mellitus (T2D) in human subjects and related prediabetes conditions such as hyperglycemia.
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
Determining relative relationships of people who share a common ancestor within at least a threshold number of generations includes: receiving recombinable deoxyribonucleic acid (DNA) sequence information of a first user and recombinable DNA sequence information of a plurality of users; processing, using one or more computer processors, the recombinable DNA sequence information of the plurality of users in parallel; determining, based at least in part on a result of processing the recombinable DNA information of the plurality of users in parallel, a predicted degree of relationship between the first user and a user among the plurality of users, the predicted degree of relative relationship corresponding to a number of generations within which the first user and the second user share a common ancestor.
Assembling a cohort includes: receiving genetic characteristic information pertaining to a desired genetic characteristic; using the genetic characteristic information to search a data storage comprising information of previously genotyped individuals to derive a candidate group having the desired genetic characteristic; and assembling the cohort based at least in part on the candidate group.
Sharing data is disclosed. In some cases, sharing data includes receiving a request to share data from a first account to a second account, receiving an indication of a plurality of first account profiles associated with the first account to share with the second account, and establishing sharing from the plurality of first account profiles to the second account, wherein sharing comprises the second account having read access to a subset of nonpublic data associated with the plurality of first account profiles.
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
G06F 16/9535 - Search customisation based on user profiles and personalisation
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
Determining relative connections between individuals includes: obtaining identification information of a first individual and identification information of a second individual; determining, based at least in part on a relative connections graph, a relative connections path connecting the first individual, the second individual, and at least one additional individual; and outputting information pertaining to the relative connections path.
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
G16B 10/00 - ICT specially adapted for evolutionary bioinformatics, e.g. phylogenetic tree construction or analysis
G16B 99/00 - Subject matter not provided for in other groups of this subclass
G16Z 99/00 - Subject matter not provided for in other main groups of this subclass
Displaying an indication of ancestral data is disclosed. An indication that a genetic interval corresponds to a reference interval that has a likelihood of having one or more ancestral origins is received. One or more graphic display parameters are determined based at least in part on the indication. An indication of the one or more ancestral origins is visually displayed using the one or more graphic display parameters.
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
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
G16B 45/00 - ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks
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
G16B 20/40 - Population geneticsLinkage disequilibrium
G06F 3/04812 - Interaction techniques based on cursor appearance or behaviour, e.g. being affected by the presence of displayed objects
G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
G06F 11/07 - Responding to the occurrence of a fault, e.g. fault tolerance
Determining relative relationships of people who share a common ancestor within at least a threshold number of generations includes: receiving recombinable deoxyribonucleic acid (DNA) sequence information of a first user and recombinable DNA sequence information of a plurality of users; processing, using one or more computer processors, the recombinable DNA sequence information of the plurality of users in parallel; determining, based at least in part on a result of processing the recombinable DNA information of the plurality of users in parallel, a predicted degree of relationship between the first user and a user among the plurality of users, the predicted degree of relative relationship corresponding to a number of generations within which the first user and the second user share a common ancestor.
The present disclosure provides binding proteins, such as antibodies and antigen-binding fragments, which specifically bind to human CD96 receptor protein (hu-CD96) and are capable of decreasing, inhibiting, and/or fully-blocking immune regulatory effects mediated by hu-CD96. The present disclosure also provides methods of using the antibodies (and compositions thereof) to treat diseases and conditions responsive to decreasing, inhibiting and/or blocking immune regulatory function or activity mediated by CD96 binding to CD155, including effects arising from CD96 interactions with CD226 and/or TIGIT.
C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
A method, software, database, and system in which a query attribute is used as the basis for accessing stored attribute combinations and their frequencies of occurrence for individuals; and tabulating, based on frequencies of occurrence, those attribute combinations that are most likely to co-occur with the query attribute.
G06F 16/9535 - Search customisation based on user profiles and personalisation
G06F 16/2457 - Query processing with adaptation to user needs
G16H 20/30 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
G16H 40/63 - 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 operation of medical equipment or devices for local operation
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
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
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
G16B 20/40 - Population geneticsLinkage disequilibrium
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks
Admixture generation determination includes: obtaining ancestry assignment information associated with an individual's genotype data, the ancestry assignment information at least indicating that a portion of the individual's genotype data is deemed to be associated with a specific ancestry; determining the individual's genetic ancestry summary data corresponding to the specific ancestry; estimating an admixture generation associated with the specific ancestry, the admixture generation indicating a most recent generation or a most recent generation range from which the individual has at least one non-admixed ancestor of the specific ancestry, the estimation including a maximum likelihood determination based at least in part on the individual's genetic ancestry summary data and a recombination model; and outputting the estimated admixture generation.
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 10/00 - ICT specially adapted for evolutionary bioinformatics, e.g. phylogenetic tree construction or analysis
05 - Pharmaceutical, veterinary and sanitary products
35 - Advertising and business services
Goods & Services
Prescription and non-prescription medicines, namely, pills, tablets, capsules, caplets, liquid drops, sachets and pharmaceutical preparations for the treatment of anxiety, depression, insomnia, sexual dysfunction, seasonal affective disorder, birth control, hair loss, acne, urinary tract infections, cold sores, herpes, asthma, acid reflux, high blood pressure, hot flashes, migraines, skin dark spots, hypothyroidism, smoking, sinus infections, blood cholesterol, sexually transmitted diseases; pharmaceutical preparations in the nature of blood tests for blood sugar, blood cholesterol, and blood type Pharmaceutical services, namely, processing online and telephone prescription orders in retail and central fill pharmacies
05 - Pharmaceutical, veterinary and sanitary products
35 - Advertising and business services
Goods & Services
Prescription and non-prescription medicines, namely, pills, tablets, capsules, caplets, liquid drops, sachets and pharmaceutical preparations for the treatment of anxiety, depression, insomnia, sexual dysfunction, seasonal affective disorder, birth control, hair loss, acne, urinary tract infections, cold sores, herpes, asthma, acid reflux, high blood pressure, hot flashes, migraines, skin dark spots, hypothyroidism, smoking, sinus infections, blood cholesterol, sexually transmitted diseases; pharmaceutical preparations in the nature of blood tests for blood sugar, blood cholesterol, and blood type Pharmaceutical services, namely, processing online and telephone prescription orders in retail and central fill pharmacies
05 - Pharmaceutical, veterinary and sanitary products
35 - Advertising and business services
Goods & Services
Prescription and non-prescription medicines, namely, pills, tablets, capsules, caplets, liquid drops, sachets and pharmaceutical preparations for the treatment of anxiety, depression, insomnia, sexual dysfunction, seasonal affective disorder, birth control, hair loss, acne, urinary tract infections, cold sores, herpes, asthma, acid reflux, high blood pressure, hot flashes, migraines, skin dark spots, hypothyroidism, smoking, sinus infections, blood cholesterol, sexually transmitted diseases; pharmaceutical preparations in the nature of blood tests for blood sugar, blood cholesterol, and blood type Pharmaceutical services, namely, processing online and telephone prescription orders in retail and central fill pharmacies
64.
Display screen or portion thereof with graphical user interface
A method, software, database and system for attribute partner identification and social network based attribute analysis are presented in which attribute profiles associated with individuals can be compared and potential partners identified. Connections can be formed within social networks based on analysis of genetic and non-genetic data. Degrees of attribute separation (genetic and non-genetic) can be utilized to analyze relationships and to identify individuals who might benefit from being connected.
G06F 16/9535 - Search customisation based on user profiles and personalisation
G06F 16/2457 - Query processing with adaptation to user needs
G16H 20/30 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
G16H 40/63 - 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 operation of medical equipment or devices for local operation
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
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
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
G16B 20/40 - Population geneticsLinkage disequilibrium
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks
Displaying an indication of ancestral data is disclosed. An indication that a genetic interval corresponds to a reference interval that has a likelihood of having one or more ancestral origins is received. One or more graphic display parameters are determined based at least in part on the indication. An indication of the one or more ancestral origins is visually displayed using the one or more graphic display parameters.
G06F 3/048 - Interaction techniques based on graphical user interfaces [GUI]
G06F 3/04812 - Interaction techniques based on cursor appearance or behaviour, e.g. being affected by the presence of displayed objects
G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
G16B 20/40 - Population geneticsLinkage disequilibrium
67.
Learning System for Pangenetic-Based Recommendations
An embodiment may involve storing, by a computing device and in a database, a set of pangenetic attributes of a set of individuals, wherein the pangenetic attributes of the set are respectively and statistically associated with products; based on the statistical associations between the pangenetic attributes and the products, determining, by the computing device, product recommendations for a second set of individuals; receiving, by the computing device and from the second set of individuals, a plurality of measures of satisfaction with the product recommendations; based on the plurality of measures of satisfaction, learning, by the computing device, an association between a subset of the pangenetic attributes and a particular product; and storing, by the computing device and in the database, the learned association, wherein the learned association provides a basis for subsequent recommendations of the particular product when a subsequent individual exhibits the subset of the pangenetic attributes.
Determining relative relationships of people who share a common ancestor within at least a threshold number of generations includes: receiving recombinable deoxyribonucleic acid (DNA) sequence information of a first user and recombinable DNA sequence information of a plurality of users; processing, using one or more computer processors, the recombinable DNA sequence information of the plurality of users in parallel; determining, based at least in part on a result of processing the recombinable DNA information of the plurality of users in parallel, a predicted degree of relationship between the first user and a user among the plurality of users, the predicted degree of relative relationship corresponding to a number of generations within which the first user and the second user share a common ancestor.
A method, software, database and system for determining an optimal treatment for an illness in an individual and for determining the impact (e.g., side effects and intended benefits) of the treatment in the individual are presented in which an attribute profile of the individual containing genetic and non-genetic attributes is compared against a database containing combinations genetic and non-genetic attributes that are statistically associated with successful treatment of the illness in other individuals.
G06F 16/9535 - Search customisation based on user profiles and personalisation
G06F 16/2457 - Query processing with adaptation to user needs
G16H 20/30 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
G16H 40/63 - 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 operation of medical equipment or devices for local operation
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
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
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
G16B 20/40 - Population geneticsLinkage disequilibrium
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
70.
Attribute combination discovery for predisposition determination of health conditions
A method, software, database, and system in which a query attribute is used as the basis for accessing stored attribute combinations and their frequencies of occurrence for individuals; and tabulating, based on frequencies of occurrence, those attribute combinations that are most likely to co-occur with the query attribute.
G16H 70/20 - ICT specially adapted for the handling or processing of medical references relating to practices or guidelines
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 40/63 - 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 operation of medical equipment or devices for local operation
G06F 16/9535 - Search customisation based on user profiles and personalisation
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 20/30 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
G16B 20/40 - Population geneticsLinkage disequilibrium
G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks
Displaying an indication of ancestral data is disclosed. An indication that a genetic interval corresponds to a reference interval that has a likelihood of having one or more ancestral origins is received. One or more graphic display parameters are determined based at least in part on the indication. An indication of the one or more ancestral origins is visually displayed using the one or more graphic display parameters.
G06F 3/048 - Interaction techniques based on graphical user interfaces [GUI]
G06F 3/04812 - Interaction techniques based on cursor appearance or behaviour, e.g. being affected by the presence of displayed objects
G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
G16B 20/40 - Population geneticsLinkage disequilibrium
Ancestry deconvolution includes obtaining unphased genotype data of an individual; phasing, using one or more processors, the unphased genotype data to generate phased haplotype data; using a learning machine to classify portions of the phased haplotype data as corresponding to specific ancestries respectively and generate initial classification results; and correcting errors in the initial classification results to generate modified classification results.
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
41 - Education, entertainment, sporting and cultural services
Goods & Services
Kits for use in genetic testing comprising a saliva collection tube, caps for tube, for use in DNA testing of humans. Providing an online resource center, namely, online articles and papers in the fields of genetic testing and genotype technologies; providing a website featuring online publications in the nature of articles, journals, brochures, leaflets, guides and manuals in the fields of genetic testing and genotype technologies; providing an online publication in the nature of an interactive encyclopedia in the fields of genetic testing and genotype technologies.
74.
Display screen or portion thereof with graphical user interface
A method, software, database, and system in which a query attribute is used as the basis for accessing stored attribute combinations and their frequencies of occurrence for individuals; and tabulating, based on frequencies of occurrence, those attribute combinations that are most likely to co-occur with the query attribute.
G06F 7/00 - Methods or arrangements for processing data by operating upon the order or content of the data handled
G16H 70/20 - ICT specially adapted for the handling or processing of medical references relating to practices or guidelines
G16H 20/30 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
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 50/70 - 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
G16B 20/40 - Population geneticsLinkage disequilibrium
G06F 16/2457 - Query processing with adaptation to user needs
G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
G06N 7/00 - Computing arrangements based on specific mathematical models
G06F 16/00 - Information retrievalDatabase structures thereforFile system structures therefor
G16H 40/63 - 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 operation of medical equipment or devices for local operation
G06F 16/9535 - Search customisation based on user profiles and personalisation
G06F 16/955 - Retrieval from the web using information identifiers, e.g. uniform resource locators [URL]
A method, software, database and system for attribute partner identification and social network based attribute analysis are presented in which attribute profiles associated with individuals can be compared and potential partners identified. Connections can be formed within social networks based on analysis of genetic and non-genetic data. Degrees of attribute separation (genetic and non-genetic) can be utilized to analyze relationships and to identify individuals who might benefit from being connected.
G06F 16/9535 - Search customisation based on user profiles and personalisation
G06F 16/2457 - Query processing with adaptation to user needs
G16H 20/30 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
G16H 40/63 - 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 operation of medical equipment or devices for local operation
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
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
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
G16B 20/40 - Population geneticsLinkage disequilibrium
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
42 - Scientific, technological and industrial services, research and design
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
Providing a website featuring temporary use of non-downloadable software for obtaining medical and healthcare services; platform as a service (PAAS) featuring computer software platforms and mobile software platforms for obtaining medical and healthcare services Medical services, namely, providing on-line medical consultancy services; providing online information in the field of healthcare; pharmacy services, namely, dispensing of pharmaceuticals
42 - Scientific, technological and industrial services, research and design
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
Providing a website featuring temporary use of non-downloadable software for obtaining medical and healthcare services; platform as a service (PAAS) featuring computer software platforms and mobile software platforms for obtaining medical and healthcare services Medical services, namely, providing on-line medical consultancy services; providing online information in the field of healthcare; pharmacy services, namely, dispensing of pharmaceuticals
42 - Scientific, technological and industrial services, research and design
44 - Medical, veterinary, hygienic and cosmetic services; agriculture, horticulture and forestry services
Goods & Services
Providing a website featuring temporary use of non-downloadable software for obtaining medical and healthcare services; platform as a service (PAAS) featuring computer software platforms and mobile software platforms for obtaining medical and healthcare services Medical services, namely, providing on-line medical consultancy services; providing online information in the field of healthcare; pharmacy services, namely, dispensing of pharmaceuticals
80.
Computer implemented predisposition prediction in a genetics platform
A method, software, database and system for attribute partner identification and social network based attribute analysis are presented in which attribute profiles associated with individuals can be compared and potential partners identified. Connections can be formed within social networks based on analysis of genetic and non-genetic data. Degrees of attribute separation (genetic and non-genetic) can be utilized to analyze relationships and to identify individuals who might benefit from being connected.
G06F 16/9535 - Search customisation based on user profiles and personalisation
G06F 16/2457 - Query processing with adaptation to user needs
G16H 20/30 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
G16H 40/63 - 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 operation of medical equipment or devices for local operation
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
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
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
G16B 20/40 - Population geneticsLinkage disequilibrium
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
81.
Computer implemented identification of modifiable attributes associated with phenotypic predispositions in a genetics platform
A method, software, database and system for attribute partner identification and social network based attribute analysis are presented in which attribute profiles associated with individuals can be compared and potential partners identified. Connections can be formed within social networks based on analysis of genetic and non-genetic data. Degrees of attribute separation (genetic and non-genetic) can be utilized to analyze relationships and to identify individuals who might benefit from being connected.
G06F 16/9535 - Search customisation based on user profiles and personalisation
G06F 16/2457 - Query processing with adaptation to user needs
G16H 20/30 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
G16H 40/63 - 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 operation of medical equipment or devices for local operation
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
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
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
G16B 20/40 - Population geneticsLinkage disequilibrium
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
82.
Computer implemented identification of treatments for predicted predispositions with clinician assistance
A method, software, database and system for attribute partner identification and social network based attribute analysis are presented in which attribute profiles associated with individuals can be compared and potential partners identified. Connections can be formed within social networks based on analysis of genetic and non-genetic data. Degrees of attribute separation (genetic and non-genetic) can be utilized to analyze relationships and to identify individuals who might benefit from being connected.
G06F 16/9535 - Search customisation based on user profiles and personalisation
G06F 16/2457 - Query processing with adaptation to user needs
G16H 20/30 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
G16H 40/63 - 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 operation of medical equipment or devices for local operation
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
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
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
G16B 20/40 - Population geneticsLinkage disequilibrium
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
The present disclosure relates to an inhibitor of human TRPM3 for use in the treatment or prevention of migraine. Combination therapies are also described. In other aspects, the present disclosure provides methods for identifying suitable patients, methods for identifying inhibitors of human TRPM3 and cell lines for use in such methods.
The disclosed embodiments concern methods, apparatus, systems and computer program products for determining and displaying pedigrees based on IBD data. Some implementations use a probabilistic relationship model to obtain various likelihoods of various potential relationships based on pairwise IBD data, and pairwise age data. Some implementations build large pedigrees by combining smaller pedigrees. Some implementations display pedigree graphs with various features that are informative and easy to understand.
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
85.
Display screen or portion thereof with graphical user interface
A method, software, database and system for attribute partner identification and social network based attribute analysis are presented in which attribute profiles associated with individuals can be compared and potential partners identified. Connections can be formed within social networks based on analysis of genetic and non-genetic data. Degrees of attribute separation (genetic and non-genetic) can be utilized to analyze relationships and to identify individuals who might benefit from being connected.
G06F 16/9535 - Search customisation based on user profiles and personalisation
G06F 16/2457 - Query processing with adaptation to user needs
G16H 20/30 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
G16H 40/63 - 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 operation of medical equipment or devices for local operation
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
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
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
G16B 20/40 - Population geneticsLinkage disequilibrium
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
88.
Computer implemented identification of modifiable attributes associated with phenotypic predispositions in a genetics platform
A method, software, database and system for attribute partner identification and social network based attribute analysis are presented in which attribute profiles associated with individuals can be compared and potential partners identified. Connections can be formed within social networks based on analysis of genetic and non-genetic data. Degrees of attribute separation (genetic and non-genetic) can be utilized to analyze relationships and to identify individuals who might benefit from being connected.
G06F 16/9535 - Search customisation based on user profiles and personalisation
G06F 16/2457 - Query processing with adaptation to user needs
G16H 20/30 - ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
G16H 40/63 - 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 operation of medical equipment or devices for local operation
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
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
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
G16B 20/40 - Population geneticsLinkage disequilibrium
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
89.
Genetic comparisons between grandparents and grandchildren
Displaying a comparison of genotypic information between relatives is disclosed, including receiving an indication that a first individual is a grandparent, receiving an indication that a second individual is a grandchild of the first individual, comparing the genotypic information of the first individual and the second individual and calculating a similarity score, and displaying an indication of the similarity score graphically using colors.
Determining relative relationships of people who share a common ancestor within at least a threshold number of generations includes: receiving recombinable deoxyribonucleic acid (DNA) sequence information of a first user and recombinable DNA sequence information of a plurality of users; processing, using one or more computer processors, the recombinable DNA sequence information of the plurality of users in parallel; determining, based at least in part on a result of processing the recombinable DNA information of the plurality of users in parallel, a predicted degree of relationship between the first user and a user among the plurality of users, the predicted degree of relative relationship corresponding to a number of generations within which the first user and the second user share a common ancestor.
The disclosed embodiments concern methods, apparatus, systems, and computer program products for developing polygenic risk score (PRS) models with improved performance across different ethnicities and for different target phenotypes.
C12Q 1/6827 - Hybridisation assays for detection of mutation or polymorphism
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
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
The disclosed embodiments concern methods, apparatus, systems, and computer program products for storing and retrieving genetic data for individuals. In some implementations, a storage format is provided that allows genetic data to be defined by metadata for reproduce-ability.
G16B 50/30 - Data warehousingComputing architectures
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
G16B 20/00 - ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
G16B 50/00 - ICT programming tools or database systems specially adapted for bioinformatics
The disclosed embodiments concern methods, apparatus, systems, and computer program products for storing and retrieving genetic data for individuals. In some implementations, a storage format is provided that allows genetic data to be defined by metadata for reproduce-ability. In some embodiments, the method further includes accessing the database to identify genetic data for a plurality of the one or more individuals having one or more preselected phenotypes; assembling a cases cohort including a first plurality of individuals having the one or more preselected phenotypes; assembling a control cohort including a second plurality of individuals not in the cases cohort; and performing a genome wide association study (GWAS) based on the genetic data from the database for individuals in the cases cohort and control cohort.
The present invention provides binding proteins, such as antibodies and antigen-binding fragments, which specifically bind to human interleukin-1 receptor accessory protein (hu-IL1RAP) and fully block the IL-1, IL-33, and IL-36 intracellular signaling pathways. Compositions comprising such binding proteins and methods of making and using such binding proteins are also provided.
C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
A61K 39/00 - Medicinal preparations containing antigens or antibodies
Sharing data is disclosed. In some cases, sharing data includes receiving a request to share data from a first account to a second account, receiving an indication of a plurality of first account profiles associated with the first account to share with the second account, and establishing sharing from the plurality of first account profiles to the second account, wherein sharing comprises the second account having read access to a subset of nonpublic data associated with the plurality of first account profiles.
G06F 16/9535 - Search customisation based on user profiles and personalisation
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
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
Presenting ancestral origin information, comprising: receiving a request to display ancestry data of an individual; obtaining ancestry composition information of the individual, the ancestry composition information including information pertaining to a proportion of the individual's genotype data that is deemed to correspond to a specific ancestry; and presenting the ancestry composition information to be displayed.
G06F 16/28 - Databases characterised by their database models, e.g. relational or object models
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
G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks
Presenting ancestral origin information, comprising: receiving a request to display ancestry data of an individual; obtaining ancestry composition information of the individual, the ancestry composition information including information pertaining to a proportion of the individual's genotype data that is deemed to correspond to a specific ancestry; and presenting the ancestry composition information to be displayed.
The disclosed embodiments concern methods, apparatus, systems, and computer program products for developing polygenic risk score (PRS) models with improved performance across different ethnicities and for different target phenotypes.
G16B 20/20 - Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
G06F 30/27 - Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
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
100.
Anti-CD200R1 antibodies and methods of use thereof
The present disclosure provides binding proteins, such as antibodies and antigen-binding fragments, which specifically bind to human CD200R1 receptor protein (hu-CD200R1) and are capable of decreasing, inhibiting, and/or fully-blocking immune regulatory effects mediated by hu-CD200R1. The present disclosure also provides methods of using the antibodies (and compositions thereof) to treat diseases and conditions responsive to decreasing, inhibiting and/or blocking immune regulatory function or activity mediated by CD200 binding to CD200R1.
C07K 16/28 - Immunoglobulins, e.g. monoclonal or polyclonal antibodies against material from animals or humans against receptors, cell surface antigens or cell surface determinants
