SG11201911805VA - Deep learning-based variant classifier - Google Patents

Deep learning-based variant classifier

Info

Publication number
SG11201911805VA
SG11201911805VA SG11201911805VA SG11201911805VA SG11201911805VA SG 11201911805V A SG11201911805V A SG 11201911805VA SG 11201911805V A SG11201911805V A SG 11201911805VA SG 11201911805V A SG11201911805V A SG 11201911805VA SG 11201911805V A SG11201911805V A SG 11201911805VA
Authority
SG
Singapore
Prior art keywords
deep learning
based variant
variant classifier
classifier
learning
Prior art date
Application number
SG11201911805VA
Inventor
Ole Benjamin Schulz-Trieglaff
Anthony James Cox
Kai-How FARH
Original Assignee
Illumina Inc
Illumina Cambridge Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Illumina Inc, Illumina Cambridge Ltd filed Critical Illumina Inc
Publication of SG11201911805VA publication Critical patent/SG11201911805VA/en

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Classifications

    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20Supervised data analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00Pattern recognition
    • G06F18/20Analysing
    • G06F18/21Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G06F18/2148Generating training patterns; Bootstrap methods, e.g. bagging or boosting characterised by the process organisation or structure, e.g. boosting cascade
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00Pattern recognition
    • G06F18/20Analysing
    • G06F18/24Classification techniques
    • G06F18/243Classification techniques relating to the number of classes
    • G06F18/2431Multiple classes
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00Arrangements for program control, e.g. control units
    • G06F9/06Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/30Arrangements for executing machine instructions, e.g. instruction decode
    • G06F9/38Concurrent instruction execution, e.g. pipeline, look ahead
    • G06F9/3877Concurrent instruction execution, e.g. pipeline, look ahead using a slave processor, e.g. coprocessor
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/04Architecture, e.g. interconnection topology
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/04Architecture, e.g. interconnection topology
    • G06N3/045Combinations of networks
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/08Learning methods
    • G06N3/084Backpropagation, e.g. using gradient descent
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
SG11201911805VA 2018-01-15 2019-01-14 Deep learning-based variant classifier SG11201911805VA (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US201862617552P 2018-01-15 2018-01-15
PCT/US2019/013534 WO2019140402A1 (en) 2018-01-15 2019-01-14 Deep learning-based variant classifier

Publications (1)

Publication Number Publication Date
SG11201911805VA true SG11201911805VA (en) 2020-01-30

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US (2) US11705219B2 (en)
EP (2) EP3619653B1 (en)
JP (3) JP6862581B2 (en)
KR (2) KR102273717B1 (en)
CN (1) CN110832510A (en)
AU (3) AU2019206709B2 (en)
CA (1) CA3065939A1 (en)
IL (2) IL283427B2 (en)
NZ (1) NZ759659A (en)
SG (1) SG11201911805VA (en)
WO (1) WO2019140402A1 (en)

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EP3901833A1 (en) 2021-10-27
US20230386611A1 (en) 2023-11-30
JP7247253B2 (en) 2023-03-28
AU2021282469B2 (en) 2023-07-20
AU2019206709B2 (en) 2021-09-09
IL283427A (en) 2021-06-30
NZ759659A (en) 2022-07-01
WO2019140402A1 (en) 2019-07-18
JP6862581B2 (en) 2021-04-21
AU2023251541A1 (en) 2023-11-16
IL283427B1 (en) 2023-06-01
AU2019206709A1 (en) 2019-12-19
CA3065939A1 (en) 2019-07-18
JP2020525893A (en) 2020-08-27
IL283427B2 (en) 2023-10-01
AU2021282469A1 (en) 2022-01-06
JP2021120869A (en) 2021-08-19
KR102273717B1 (en) 2021-07-06
JP2023080096A (en) 2023-06-08
US11705219B2 (en) 2023-07-18
EP3619653B1 (en) 2021-05-19
CN110832510A (en) 2020-02-21
US20190220704A1 (en) 2019-07-18
KR20200010488A (en) 2020-01-30
KR20210084686A (en) 2021-07-07
IL271093A (en) 2020-01-30
IL271093B (en) 2021-09-30
EP3619653A1 (en) 2020-03-11

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