IL282689A - מסווג פתוגניות של ויראנטים שמאומן למנוע התלבשות על מקומם של מטריצות תדרים - Google Patents

מסווג פתוגניות של ויראנטים שמאומן למנוע התלבשות על מקומם של מטריצות תדרים

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Publication number
IL282689A
IL282689A IL282689A IL28268921A IL282689A IL 282689 A IL282689 A IL 282689A IL 282689 A IL282689 A IL 282689A IL 28268921 A IL28268921 A IL 28268921A IL 282689 A IL282689 A IL 282689A
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Israel
Prior art keywords
classifier trained
position frequency
avoid overfitting
frequency matrices
pathogenicity classifier
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Application number
IL282689A
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English (en)
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IL282689B1 (he
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Illumina Inc
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Publication date
Priority claimed from PCT/US2018/055881 external-priority patent/WO2019079182A1/en
Priority claimed from US16/407,149 external-priority patent/US10540591B2/en
Application filed by Illumina Inc filed Critical Illumina Inc
Publication of IL282689A publication Critical patent/IL282689A/he
Publication of IL282689B1 publication Critical patent/IL282689B1/he

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    • 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
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Neural networks
    • G06N3/08Learning methods
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00Pattern recognition
    • G06F18/20Analysing
    • G06F18/24Classification techniques
    • G06F18/241Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2413Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
    • G06F18/24133Distances to prototypes
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00Machine learning
    • 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
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00Arrangements for image or video recognition or understanding
    • G06V10/40Extraction of image or video features
    • G06V10/44Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • G06V10/443Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components by matching or filtering
    • G06V10/449Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters
    • G06V10/451Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters with interaction between the filter responses, e.g. cortical complex cells
    • G06V10/454Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00Arrangements for image or video recognition or understanding
    • G06V10/70Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • 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
    • G16B20/00ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/30Detection of binding sites or motifs
    • 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
    • G16B30/00ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • G16B30/10Sequence alignment; Homology search
    • 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
    • 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
    • G16B50/00ICT programming tools or database systems specially adapted for bioinformatics
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00Machine learning
    • G06N20/20Ensemble learning
    • 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
    • G06N3/044Recurrent networks, e.g. Hopfield networks
    • 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
    • G06N3/048Activation functions
    • 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
    • G06N3/082Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Biophysics (AREA)
  • Medical Informatics (AREA)
  • Evolutionary Computation (AREA)
  • Molecular Biology (AREA)
  • Data Mining & Analysis (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Biology (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • General Physics & Mathematics (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Biotechnology (AREA)
  • Software Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Computing Systems (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Analytical Chemistry (AREA)
  • Chemical & Material Sciences (AREA)
  • Biomedical Technology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Mathematical Physics (AREA)
  • Computational Linguistics (AREA)
  • Genetics & Genomics (AREA)
  • Databases & Information Systems (AREA)
  • Bioethics (AREA)
  • Multimedia (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Epidemiology (AREA)
  • Public Health (AREA)
  • Image Analysis (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
  • Machine Translation (AREA)
  • Investigating Or Analysing Biological Materials (AREA)
IL282689A 2018-10-15 2019-05-09 מסווג פתוגניות של ויראנטים שמאומן למנוע התלבשות על מקומם של מטריצות תדרים IL282689B1 (he)

Applications Claiming Priority (8)

Application Number Priority Date Filing Date Title
PCT/US2018/055881 WO2019079182A1 (en) 2017-10-16 2018-10-15 SEMI-SUPERVISED APPRENTICESHIP FOR THE LEARNING OF A SET OF NEURONAL NETWORKS WITH DEEP CONVOLUTION
PCT/US2018/055878 WO2019079180A1 (en) 2017-10-16 2018-10-15 NEURONAL NETWORKS WITH DEEP CONVOLUTION OF VARIANT CLASSIFICATION
US16/160,903 US10423861B2 (en) 2017-10-16 2018-10-15 Deep learning-based techniques for training deep convolutional neural networks
PCT/US2018/055840 WO2019079166A1 (en) 2017-10-16 2018-10-15 TECHNIQUES BASED ON DEEP LEARNING LEARNING OF NEURONAL NETWORKS WITH DEEP CONVOLUTION
US16/160,986 US11315016B2 (en) 2017-10-16 2018-10-15 Deep convolutional neural networks for variant classification
US16/160,968 US11798650B2 (en) 2017-10-16 2018-10-15 Semi-supervised learning for training an ensemble of deep convolutional neural networks
US16/407,149 US10540591B2 (en) 2017-10-16 2019-05-08 Deep learning-based techniques for pre-training deep convolutional neural networks
PCT/US2019/031621 WO2020081122A1 (en) 2018-10-15 2019-05-09 Deep learning-based techniques for pre-training deep convolutional neural networks

Publications (2)

Publication Number Publication Date
IL282689A true IL282689A (he) 2021-06-30
IL282689B1 IL282689B1 (he) 2024-10-01

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IL282689A IL282689B1 (he) 2018-10-15 2019-05-09 מסווג פתוגניות של ויראנטים שמאומן למנוע התלבשות על מקומם של מטריצות תדרים
IL271091A IL271091B (he) 2018-10-15 2019-12-02 שיטות המבוססות על למידת עומק לצורך טרום–אימון של רשתות עצביות עמוקות ומורכבות

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Country Status (8)

Country Link
JP (3) JP6888123B2 (he)
KR (1) KR102165734B1 (he)
CN (2) CN111328419B (he)
AU (2) AU2019272062B2 (he)
IL (2) IL282689B1 (he)
NZ (1) NZ759665A (he)
SG (2) SG10202108013QA (he)
WO (1) WO2020081122A1 (he)

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CN111830408B (zh) * 2020-06-23 2023-04-18 朗斯顿科技(北京)有限公司 一种基于边缘计算和深度学习的电机故障诊断系统及方法
CN112003735B (zh) * 2020-07-28 2021-11-09 四川大学 一种感知风险的深度学习驱动的极限传输容量调整方法
CN112183088B (zh) * 2020-09-28 2023-11-21 云知声智能科技股份有限公司 词语层级确定的方法、模型构建方法、装置及设备
KR102279056B1 (ko) * 2021-01-19 2021-07-19 주식회사 쓰리빌리언 지식전이를 이용한 유전자변이의 병원성 예측 시스템
CN113299345B (zh) * 2021-06-30 2024-05-07 中国人民解放军军事科学院军事医学研究院 病毒基因分类的方法、装置及电子设备
CN113539354B (zh) * 2021-07-19 2023-10-27 浙江理工大学 一种高效预测革兰氏阴性菌ⅲ型和ⅳ型效应蛋白的方法
CN113822342B (zh) * 2021-09-02 2023-05-30 湖北工业大学 一种安全图卷积网络的文献分类方法及系统
CN113836892B (zh) * 2021-09-08 2023-08-08 灵犀量子(北京)医疗科技有限公司 样本量数据提取方法、装置、电子设备及存储介质
CN113963746B (zh) * 2021-09-29 2023-09-19 西安交通大学 一种基于深度学习的基因组结构变异检测系统及方法
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CN116153396A (zh) * 2023-04-21 2023-05-23 鲁东大学 一种基于迁移学习的非编码变异预测方法
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Publication number Publication date
CN113705585A (zh) 2021-11-26
JP2021152907A (ja) 2021-09-30
JP2023052011A (ja) 2023-04-11
NZ759665A (en) 2022-07-01
SG10202108013QA (en) 2021-09-29
IL271091A (he) 2020-04-30
CN111328419B (zh) 2021-10-19
IL282689B1 (he) 2024-10-01
AU2019272062A1 (en) 2020-04-30
JP7200294B2 (ja) 2023-01-06
AU2021269351B2 (en) 2023-12-14
AU2021269351A1 (en) 2021-12-09
SG11201911777QA (en) 2020-05-28
JP6888123B2 (ja) 2021-06-16
AU2019272062B2 (en) 2021-08-19
IL271091B (he) 2021-05-31
WO2020081122A1 (en) 2020-04-23
KR102165734B1 (ko) 2020-10-14
JP7515559B2 (ja) 2024-07-12
JP2021501923A (ja) 2021-01-21
KR20200044731A (ko) 2020-04-29
CN111328419A (zh) 2020-06-23

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