MX2019014689A - Clasificacion de sitio de escision y empalme basado en aprendizaje profundo. - Google Patents

Clasificacion de sitio de escision y empalme basado en aprendizaje profundo.

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Publication number
MX2019014689A
MX2019014689A MX2019014689A MX2019014689A MX2019014689A MX 2019014689 A MX2019014689 A MX 2019014689A MX 2019014689 A MX2019014689 A MX 2019014689A MX 2019014689 A MX2019014689 A MX 2019014689A MX 2019014689 A MX2019014689 A MX 2019014689A
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Mexico
Prior art keywords
residual blocks
convolution
based classifier
neural network
convolutional neural
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Application number
MX2019014689A
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English (en)
Inventor
Kai-How FARH
Kishore Jaganathan
Panagiotopoulou Sofia Kyriazopoulou
Jeremy Francis Mcrae
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Illumina Inc
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Application filed by Illumina Inc filed Critical Illumina Inc
Publication of MX2019014689A publication Critical patent/MX2019014689A/es

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/30—Unsupervised data analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/047—Probabilistic or stochastic networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/048—Activation functions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B15/00—ICT specially adapted for analysing two-dimensional or three-dimensional molecular structures, e.g. structural or functional relations or structure alignment
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • G16B30/10—Sequence alignment; Homology search
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B50/00—ICT programming tools or database systems specially adapted for bioinformatics
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Theoretical Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Biophysics (AREA)
  • Data Mining & Analysis (AREA)
  • Medical Informatics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Software Systems (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Biotechnology (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • Mathematical Physics (AREA)
  • Computational Linguistics (AREA)
  • Biomedical Technology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Bioethics (AREA)
  • Databases & Information Systems (AREA)
  • Chemical & Material Sciences (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Analytical Chemistry (AREA)
  • Epidemiology (AREA)
  • Public Health (AREA)
  • Genetics & Genomics (AREA)
  • Probability & Statistics with Applications (AREA)
  • Crystallography & Structural Chemistry (AREA)
  • Image Analysis (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Investigating Or Analysing Biological Materials (AREA)
  • Error Detection And Correction (AREA)

Abstract

La tecnología divulgada se refiere a la construcción de un clasificador convolucional basado en redes neuronales para la clasificación de variantes. En particular, se relaciona con el entrenamiento de un clasificador basado en redes neuronales convolucionales sobre datos de entrenamiento usando una técnica de actualización de gradiente basada en propagación inversa que empareja progresivamente las salidas del clasificador basado en la red convolucional con etiquetas correspondientes de la realidad del terreno. El clasificador basado en redes neuronales convolucionales comprende grupos de bloques residuales, cada grupo de bloques residuales se parametrizan mediante varios filtros de convolución en los bloques residuales, un tamaño de ventana de convolución de los bloques residuales y una tasa de convolución atroz de los bloques residuales, el tamaño de la ventana de convolución varía entre los grupos de bloques residuales, la tasa de convolución atroz varía entre grupos de bloques residuales. Los datos de entrenamiento incluyen ejemplos de entrenamiento benignos y ejemplos de entrenamiento patogénico de pares de secuencias traducidas generadas a partir de variantes benignas y variantes patogénicas.
MX2019014689A 2017-10-16 2018-10-15 Clasificacion de sitio de escision y empalme basado en aprendizaje profundo. MX2019014689A (es)

Applications Claiming Priority (5)

Application Number Priority Date Filing Date Title
US201762573125P 2017-10-16 2017-10-16
US201762573131P 2017-10-16 2017-10-16
US201762573135P 2017-10-16 2017-10-16
US201862726158P 2018-08-31 2018-08-31
PCT/US2018/055915 WO2019079198A1 (en) 2017-10-16 2018-10-15 CLASSIFICATION OF A CONNECTION SITE BASED ON DEEP LEARNING

Publications (1)

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MX2019014689A true MX2019014689A (es) 2020-10-19

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MX2019014689A MX2019014689A (es) 2017-10-16 2018-10-15 Clasificacion de sitio de escision y empalme basado en aprendizaje profundo.
MX2022014869A MX2022014869A (es) 2017-10-16 2019-12-05 Clasificacion de sitio de escision y empalme basado en aprendizaje profundo.

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US (6) US11837324B2 (es)
EP (4) EP3622525B1 (es)
JP (7) JP6840871B2 (es)
KR (6) KR102526103B1 (es)
CN (3) CN110914910A (es)
AU (5) AU2018350909B2 (es)
BR (1) BR112019027609A2 (es)
CA (1) CA3066534A1 (es)
IL (5) IL283203B2 (es)
MX (2) MX2019014689A (es)
MY (1) MY195477A (es)
NZ (3) NZ759879A (es)
SG (3) SG11201912746QA (es)
WO (3) WO2019079202A1 (es)

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