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1. WO2020089835 - ESTIMATION OF PHENOTYPES USING DNA, PEDIGREE, AND HISTORICAL DATA

Publication Number WO/2020/089835
Publication Date 07.05.2020
International Application No. PCT/IB2019/059366
International Filing Date 31.10.2019
IPC
G16B 20/00 2019.01
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
20ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
G06Q 10/04 2012.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
10Administration; Management
04Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
G06N 20/00 2019.01
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
20Machine learning
G16B 35/10 2019.01
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
35ICT specially adapted for in silico combinatorial libraries of nucleic acids, proteins or peptides
10Design of libraries
CPC
G16B 10/00
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
10ICT specially adapted for evolutionary bioinformatics, e.g. phylogenetic tree construction or analysis
G16B 20/20
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
20ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
20Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
G16B 20/40
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
20ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
40Population genetics; Linkage disequilibrium
G16B 40/20
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
40ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
20Supervised data analysis
G16B 40/30
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
40ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
30Unsupervised data analysis
G16B 5/20
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
5ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
20Probabilistic models
Applicants
  • ANCESTRY.COM DNA, LLC [US]/[US]
Inventors
  • GIRSHICK, Ahna R.
  • TELIS, Natalie
  • GRANKA, Julie M.
  • HAUG-BALTZELL, Asher Keith
  • SONG, Shiya
Agents
  • TSANG, Fredrick
  • BROWNSTONE, Daniel R.
Priority Data
62/753,75831.10.2018US
Publication Language English (EN)
Filing Language English (EN)
Designated States
Title
(EN) ESTIMATION OF PHENOTYPES USING DNA, PEDIGREE, AND HISTORICAL DATA
(FR) ESTIMATION DE PHÉNOTYPES À L'AIDE DE L'ADN, DU PEDIGREE ET DE DONNÉES HISTORIQUES
Abstract
(EN)
Disclosed are techniques for predicting a trait of an individual and identifying a set of enriched record collections of a genetic community. To predict a trait of an individual, DNA features and non-DNA features of the individual are accessed to generate a feature vector that is inputted into a machine learning model. The machine learning model generates a prediction of the trait. The prediction may be based on an inheritance prediction and/or a community prediction. To identify a set of enriched record collections, individuals belonging to a genetic community are identified and a set of candidate record collections are accessed. A community count and a background count is determined for each candidate record collection. The set of enriched record collections are identified based on a comparison of the community count and the background count. The genetic community may be annotated using the set of enriched record collections.
(FR)
La présente invention concerne des techniques permettant de prédire un trait d'un individu et d'identifier un ensemble de collections d'enregistrements enrichis d'une communauté génétique. Pour prédire un trait d'un individu, des caractéristiques d'ADN et des caractéristiques non ADN de l'individu sont accessibles pour générer un vecteur de caractéristiques qui est entré dans un modèle d'apprentissage machine. Le modèle d'apprentissage machine génère une prédiction du trait. La prédiction peut être basée sur une prédiction d'hérédité et/ou une prédiction de communauté. Pour identifier un ensemble de collections d'enregistrements enrichis, des individus appartenant à une communauté génétique sont identifiés et un ensemble de collections d'enregistrements candidats est accessible. Un compte de communauté et un compte d'arrière-plan sont déterminés pour chaque collection d'enregistrements candidate. L'ensemble de collections d'enregistrements enrichis est identifié sur la base d'une comparaison de communauté et du compte d'arrière-plan. La communauté génétique peut être annotée à l'aide de l'ensemble de collections d'enregistrements enrichis.
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