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 תדריםInfo
- 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
- Authority
- IL
- Israel
- Prior art keywords
- classifier trained
- position frequency
- avoid overfitting
- frequency matrices
- pathogenicity classifier
- Prior art date
Links
- 230000007918 pathogenicity Effects 0.000 title 1
Classifications
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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
- G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
-
- 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
- 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
-
- 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
- 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
- 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
- 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
Landscapes
- 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)
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 |
Family
ID=70283180
Family Applications (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
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 |
Family Applications After (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
IL271091A IL271091B (he) | 2018-10-15 | 2019-12-02 | שיטות המבוססות על למידת עומק לצורך טרום–אימון של רשתות עצביות עמוקות ומורכבות |
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) |
Families Citing this family (16)
Publication number | Priority date | Publication date | Assignee | Title |
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CN109543761B (zh) * | 2018-11-27 | 2020-07-07 | 成都工业学院 | 植物适生地的分类方法及装置 |
US11443832B2 (en) | 2019-03-07 | 2022-09-13 | Nvidia Corporation | Genetic mutation detection using deep learning |
KR102418073B1 (ko) * | 2020-06-08 | 2022-07-06 | 고려대학교 산학협력단 | 인공지능 기반 비디오 투시 연하검사 자동화 분석 장치 및 방법 |
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 |
US20240087683A1 (en) * | 2022-09-14 | 2024-03-14 | Microsoft Technology Licensing, Llc | Classification using a machine learning model trained with triplet loss |
CN115662520B (zh) * | 2022-10-27 | 2023-04-14 | 黑龙江金域医学检验实验室有限公司 | Bcr/abl1融合基因的检测方法及相关设备 |
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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CA2044616A1 (en) | 1989-10-26 | 1991-04-27 | Roger Y. Tsien | Dna sequencing |
US5641658A (en) | 1994-08-03 | 1997-06-24 | Mosaic Technologies, Inc. | Method for performing amplification of nucleic acid with two primers bound to a single solid support |
EP0975802B1 (en) | 1997-04-01 | 2004-06-23 | Manteia S.A. | Method of nucleic acid sequencing |
AR021833A1 (es) | 1998-09-30 | 2002-08-07 | Applied Research Systems | Metodos de amplificacion y secuenciacion de acido nucleico |
GB0006153D0 (en) * | 2000-03-14 | 2000-05-03 | Inpharmatica Ltd | Database |
CA2415897A1 (en) | 2000-07-07 | 2002-01-17 | Susan H. Hardin | Real-time sequence determination |
WO2002044425A2 (en) | 2000-12-01 | 2002-06-06 | Visigen Biotechnologies, Inc. | Enzymatic nucleic acid synthesis: compositions and methods for altering monomer incorporation fidelity |
AR031640A1 (es) | 2000-12-08 | 2003-09-24 | Applied Research Systems | Amplificacion isotermica de acidos nucleicos en un soporte solido |
US7057026B2 (en) | 2001-12-04 | 2006-06-06 | Solexa Limited | Labelled nucleotides |
US20040002090A1 (en) | 2002-03-05 | 2004-01-01 | Pascal Mayer | Methods for detecting genome-wide sequence variations associated with a phenotype |
US7541444B2 (en) | 2002-08-23 | 2009-06-02 | Illumina Cambridge Limited | Modified nucleotides |
CN101914620B (zh) | 2004-09-17 | 2014-02-12 | 加利福尼亚太平洋生命科学公司 | 核酸测序的方法 |
GB0427236D0 (en) | 2004-12-13 | 2005-01-12 | Solexa Ltd | Improved method of nucleotide detection |
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GB0514910D0 (en) | 2005-07-20 | 2005-08-24 | Solexa Ltd | Method for sequencing a polynucleotide template |
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GB0522310D0 (en) | 2005-11-01 | 2005-12-07 | Solexa Ltd | Methods of preparing libraries of template polynucleotides |
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JP5773406B2 (ja) | 2010-07-28 | 2015-09-02 | 学校法人明治大学 | Gpiアンカー型タンパク質の判定装置、判定方法及び判定プログラム |
EP2663656B1 (en) | 2011-01-13 | 2016-08-24 | Decode Genetics EHF | Genetic variants as markers for use in urinary bladder cancer risk assessment |
ES2875892T3 (es) * | 2013-09-20 | 2021-11-11 | Spraying Systems Co | Boquilla de pulverización para craqueo catalítico fluidizado |
EP3194627B1 (en) | 2014-09-18 | 2023-08-16 | Illumina, Inc. | Methods and systems for analyzing nucleic acid sequencing data |
US20190136248A1 (en) * | 2015-05-15 | 2019-05-09 | Pioneer Hi-Bred International, Inc. | Novel guide rna/cas endonuclease systems |
WO2016209999A1 (en) | 2015-06-22 | 2016-12-29 | Counsyl, Inc. | Methods of predicting pathogenicity of genetic sequence variants |
JP2017151679A (ja) | 2016-02-24 | 2017-08-31 | コニカミノルタ株式会社 | 識別装置及び識別プログラム |
CN107622182B (zh) * | 2017-08-04 | 2020-10-09 | 中南大学 | 蛋白质局部结构特征的预测方法及系统 |
CN108197427B (zh) * | 2018-01-02 | 2020-09-04 | 山东师范大学 | 基于深度卷积神经网络的蛋白质亚细胞定位方法和装置 |
CN108595909A (zh) * | 2018-03-29 | 2018-09-28 | 山东师范大学 | 基于集成分类器的ta蛋白靶向预测方法 |
-
2019
- 2019-05-09 WO PCT/US2019/031621 patent/WO2020081122A1/en active Search and Examination
- 2019-05-09 SG SG10202108013QA patent/SG10202108013QA/en unknown
- 2019-05-09 CN CN201980003263.9A patent/CN111328419B/zh active Active
- 2019-05-09 SG SG11201911777QA patent/SG11201911777QA/en unknown
- 2019-05-09 IL IL282689A patent/IL282689B1/he unknown
- 2019-05-09 AU AU2019272062A patent/AU2019272062B2/en active Active
- 2019-05-09 KR KR1020197038080A patent/KR102165734B1/ko active IP Right Grant
- 2019-05-09 JP JP2019567603A patent/JP6888123B2/ja active Active
- 2019-05-09 CN CN202111113164.1A patent/CN113705585A/zh active Pending
- 2019-05-09 NZ NZ759665A patent/NZ759665A/en unknown
- 2019-12-02 IL IL271091A patent/IL271091B/he active IP Right Grant
-
2021
- 2021-05-19 JP JP2021084634A patent/JP7200294B2/ja active Active
- 2021-11-17 AU AU2021269351A patent/AU2021269351B2/en active Active
-
2022
- 2022-12-21 JP JP2022204685A patent/JP7515559B2/ja active Active
Also Published As
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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