NZ759665A - Deep learning-based techniques for pre-training deep convolutional neural networks - Google Patents

Deep learning-based techniques for pre-training deep convolutional neural networks

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Publication number
NZ759665A
NZ759665A NZ759665A NZ75966519A NZ759665A NZ 759665 A NZ759665 A NZ 759665A NZ 759665 A NZ759665 A NZ 759665A NZ 75966519 A NZ75966519 A NZ 75966519A NZ 759665 A NZ759665 A NZ 759665A
Authority
NZ
New Zealand
Prior art keywords
training
example sequence
supplemental
sequence pairs
1hwzrun
Prior art date
Application number
NZ759665A
Other languages
English (en)
Inventor
Kai-How FARH
Hong Gao
Padigepati Samskruthi Reddy
Original Assignee
Illumina Inc
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
Priority claimed from PCT/US2018/055878 external-priority patent/WO2019079180A1/en
Priority claimed from US16/407,149 external-priority patent/US10540591B2/en
Application filed by Illumina Inc filed Critical Illumina Inc
Publication of NZ759665A publication Critical patent/NZ759665A/en

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Classifications

    • 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
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
    • G06F18/24133—Distances to prototypes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/20—Ensemble learning
    • 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
    • G06N3/045—Combinations of 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
    • G06N3/08—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
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/44—Local 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
    • G06V10/443—Local 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
    • G06V10/449—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters
    • G06V10/451—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters with interaction between the filter responses, e.g. cortical complex cells
    • G06V10/454—Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • 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
    • G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
    • 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
    • G16B20/30—Detection of binding sites or motifs
    • 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
    • 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
    • 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
    • G06N3/044—Recurrent networks, e.g. Hopfield 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
    • G06N3/082—Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections
NZ759665A 2018-10-15 2019-05-09 Deep learning-based techniques for pre-training deep convolutional neural networks NZ759665A (en)

Applications Claiming Priority (8)

Application Number Priority Date Filing Date Title
PCT/US2018/055878 WO2019079180A1 (en) 2017-10-16 2018-10-15 NEURONAL NETWORKS WITH DEEP CONVOLUTION OF VARIANT CLASSIFICATION
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
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/US2018/055840 WO2019079166A1 (en) 2017-10-16 2018-10-15 TECHNIQUES BASED ON DEEP LEARNING LEARNING OF NEURONAL NETWORKS WITH DEEP CONVOLUTION
US16/160,903 US10423861B2 (en) 2017-10-16 2018-10-15 Deep learning-based techniques for training 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 (1)

Publication Number Publication Date
NZ759665A true NZ759665A (en) 2022-07-01

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NZ759665A NZ759665A (en) 2018-10-15 2019-05-09 Deep learning-based techniques for pre-training deep convolutional neural networks

Country Status (8)

Country Link
JP (3) JP6888123B2 (ko)
KR (1) KR102165734B1 (ko)
CN (2) CN113705585A (ko)
AU (2) AU2019272062B2 (ko)
IL (2) IL271091B (ko)
NZ (1) NZ759665A (ko)
SG (2) SG11201911777QA (ko)
WO (1) WO2020081122A1 (ko)

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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
CN117688785B (zh) * 2024-02-02 2024-04-16 东北大学 一种基于种植思想的全张量重力梯度数据反演方法

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Also Published As

Publication number Publication date
SG11201911777QA (en) 2020-05-28
IL282689A (en) 2021-06-30
CN113705585A (zh) 2021-11-26
JP2021152907A (ja) 2021-09-30
CN111328419B (zh) 2021-10-19
JP6888123B2 (ja) 2021-06-16
JP7200294B2 (ja) 2023-01-06
WO2020081122A1 (en) 2020-04-23
KR20200044731A (ko) 2020-04-29
AU2021269351B2 (en) 2023-12-14
IL271091B (en) 2021-05-31
JP2021501923A (ja) 2021-01-21
AU2019272062A1 (en) 2020-04-30
IL271091A (en) 2020-04-30
CN111328419A (zh) 2020-06-23
JP2023052011A (ja) 2023-04-11
SG10202108013QA (en) 2021-09-29
KR102165734B1 (ko) 2020-10-14
AU2021269351A1 (en) 2021-12-09
AU2019272062B2 (en) 2021-08-19

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