CN112437961A - 机器学习使能的生物聚合物组装 - Google Patents

机器学习使能的生物聚合物组装 Download PDF

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Publication number
CN112437961A
CN112437961A CN201980047341.5A CN201980047341A CN112437961A CN 112437961 A CN112437961 A CN 112437961A CN 201980047341 A CN201980047341 A CN 201980047341A CN 112437961 A CN112437961 A CN 112437961A
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assembly
nucleotide
learning model
locations
biopolymer
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Chinese (zh)
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明·迪克·曹
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Quantum Si Inc
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Quantum Si Inc
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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
    • 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
    • G06—COMPUTING OR CALCULATING; 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
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • 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 [2D] or three-dimensional [3D] 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
    • 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
    • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • G16B30/20—Sequence assembly
    • 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
    • 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

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  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Theoretical Computer Science (AREA)
  • Medical Informatics (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Biophysics (AREA)
  • General Health & Medical Sciences (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Evolutionary Biology (AREA)
  • Biotechnology (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Data Mining & Analysis (AREA)
  • Chemical & Material Sciences (AREA)
  • Software Systems (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Bioethics (AREA)
  • Databases & Information Systems (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Analytical Chemistry (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Epidemiology (AREA)
  • Public Health (AREA)
  • General Engineering & Computer Science (AREA)
  • Computing Systems (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Crystallography & Structural Chemistry (AREA)
  • Molecular Biology (AREA)
  • Computational Linguistics (AREA)
  • Biomedical Technology (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
  • Apparatus Associated With Microorganisms And Enzymes (AREA)
  • Addition Polymer Or Copolymer, Post-Treatments, Or Chemical Modifications (AREA)
CN201980047341.5A 2018-05-14 2019-05-13 机器学习使能的生物聚合物组装 Pending CN112437961A (zh)

Applications Claiming Priority (5)

Application Number Priority Date Filing Date Title
US201862671260P 2018-05-14 2018-05-14
US62/671,260 2018-05-14
US201862671884P 2018-05-15 2018-05-15
US62/671,884 2018-05-15
PCT/US2019/032065 WO2019222120A1 (en) 2018-05-14 2019-05-13 Machine learning enabled biological polymer assembly

Publications (1)

Publication Number Publication Date
CN112437961A true CN112437961A (zh) 2021-03-02

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CN201980047341.5A Pending CN112437961A (zh) 2018-05-14 2019-05-13 机器学习使能的生物聚合物组装

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US (1) US20190348152A1 (https=)
EP (1) EP3794596A1 (https=)
JP (1) JP2021523479A (https=)
KR (1) KR20210010488A (https=)
CN (1) CN112437961A (https=)
AU (1) AU2019270961A1 (https=)
BR (1) BR112020022257A2 (https=)
CA (1) CA3098876A1 (https=)
MX (1) MX2020012278A (https=)
WO (1) WO2019222120A1 (https=)

Families Citing this family (4)

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Publication number Priority date Publication date Assignee Title
EP3624068A1 (en) * 2018-09-14 2020-03-18 Covestro Deutschland AG Method for improving prediction relating to the production of a polymer-ic produc
US11664090B2 (en) * 2020-06-11 2023-05-30 Life Technologies Corporation Basecaller with dilated convolutional neural network
EP4211691A1 (en) * 2020-09-11 2023-07-19 F. Hoffmann-La Roche AG Deep-learning-based techniques for generating a consensus sequence from multiple noisy sequences
CA3214755A1 (en) * 2021-04-09 2022-10-13 Natalie CASTELLANA Method for antibody identification from protein mixtures

Citations (4)

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CN102460155A (zh) * 2009-04-29 2012-05-16 考利达基因组股份有限公司 用于关于参考多核苷酸序列标注样本多核苷酸序列中的变异的方法和系统
CN103797486A (zh) * 2011-06-06 2014-05-14 皇家飞利浦有限公司 用于组装核酸序列数据的方法
US20150169824A1 (en) * 2013-12-16 2015-06-18 Complete Genomics, Inc. Basecaller for dna sequencing using machine learning
CA2894317A1 (en) * 2015-06-15 2016-12-15 Deep Genomics Incorporated Systems and methods for classifying, prioritizing and interpreting genetic variants and therapies using a deep neural network

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CN102460155A (zh) * 2009-04-29 2012-05-16 考利达基因组股份有限公司 用于关于参考多核苷酸序列标注样本多核苷酸序列中的变异的方法和系统
CN103797486A (zh) * 2011-06-06 2014-05-14 皇家飞利浦有限公司 用于组装核酸序列数据的方法
US20150169824A1 (en) * 2013-12-16 2015-06-18 Complete Genomics, Inc. Basecaller for dna sequencing using machine learning
CA2894317A1 (en) * 2015-06-15 2016-12-15 Deep Genomics Incorporated Systems and methods for classifying, prioritizing and interpreting genetic variants and therapies using a deep neural network

Non-Patent Citations (12)

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Title
ALLEX C F, ET AL.: "Neural network input representations that produce accurate consensus sequences from DNA fragment assemblies", BIOINFORMATICS, vol. 15, no. 9, 1 September 1999 (1999-09-01), pages 723 - 728, XP002267211, DOI: 10.1093/bioinformatics/15.9.723 *
HIRANUMA N, ET AL.: "DeepATAC: A deep-learning method to predict regulatory factor binding activity from ATAC-seq signals", COLD SPRING HARBOR LABORATORY, vol. 33, no. 14, 12 July 2017 (2017-07-12), pages 1 - 5 *
HOLMAN A G, ET AL.: "A Machine Learning Approach for Identifying Amino Acid Signatures in the HIV Env Gene Predictive of Dementia", PLOS ONE, vol. 7, no. 11, 14 November 2012 (2012-11-14), pages 2 *
KELLEY D R，ET AL: "Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks", GENOME RESEARCH, vol. 26, no. 7, 1 July 2016 (2016-07-01), pages 990 - 999, XP055507160, DOI: 10.1101/gr.200535.115 *
KOH P W, ET AL.: "Denoising genome-wide histone ChIP-seq with convolutional neural networks", BIOINFORMATICS, vol. 33, no. 14, 24 July 2017 (2017-07-24), pages 1 - 9 *
LOMAN N J, ET AL.: "A complete bacterial genome assembled de novo using only nanopore sequencing data", BIORXIV, 11 March 2015 (2015-03-11), pages 1 - 30 *
LOMAN N J，ET AL.: "A complete bacterial genome assembled de novo using only nanopore sequencing data", NATURE METHODS, vol. 12, no. 8, 1 August 2015 (2015-08-01), pages 733 *
LUO R B, ET AL.: "Clairvoyante: a multi-task convolutional deep neural network for variant calling in Single Molecule Sequencing", BIORXIV, vol. 10, 28 April 2018 (2018-04-28), pages 1 - 20 *
RYAN P, ET AL.: "A universal SNP and small-indel variant caller using deep neural networks", NATURE BIOTECHNOLOGY, vol. 36, no. 10, 20 March 2018 (2018-03-20), pages 983 *
RYAN P, ET AL.: "Creating a universal SNP and small indel variant caller with deep neural networks", BIORXIV, 21 December 2016 (2016-12-21), pages 1 - 13 *
UMAROV R K ，ET AL.: "Recognition of prokaryotic and eukaryotic promoters using convolutional deep learning neural networks", PLOS ONE, vol. 12, no. 2, 22 March 2017 (2017-03-22), pages 1 - 12 *
VASER R，ET AL.: "Fast and accurate de novo genome assembly from long uncorrected reads", GENOME RESEARCH, vol. 27, no. 5, 1 May 2017 (2017-05-01), pages 737 - 746, XP055608901, DOI: 10.1101/gr.214270.116 *

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Publication number Publication date
MX2020012278A (es) 2021-01-29
JP2021523479A (ja) 2021-09-02
WO2019222120A1 (en) 2019-11-21
KR20210010488A (ko) 2021-01-27
AU2019270961A1 (en) 2020-11-19
BR112020022257A2 (pt) 2021-02-23
EP3794596A1 (en) 2021-03-24
US20190348152A1 (en) 2019-11-14
CA3098876A1 (en) 2019-11-21

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Application publication date: 20210302