MX393603B - Entorno basado en el aprendizaje profundo para la identificacion de patrones de secuencia que provocan errores especificos de secuencia (ees) - Google Patents
Entorno basado en el aprendizaje profundo para la identificacion de patrones de secuencia que provocan errores especificos de secuencia (ees)Info
- Publication number
- MX393603B MX393603B MX2019015567A MX2019015567A MX393603B MX 393603 B MX393603 B MX 393603B MX 2019015567 A MX2019015567 A MX 2019015567A MX 2019015567 A MX2019015567 A MX 2019015567A MX 393603 B MX393603 B MX 393603B
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- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
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- 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
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- 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
- G06N3/045—Combinations of networks
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- 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]
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- 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
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- 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
- G06N3/082—Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections
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- 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
- G06N3/084—Backpropagation, e.g. using gradient descent
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- 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
- G06N3/09—Supervised learning
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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
- G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
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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
- G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
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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
- G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
- G16B30/10—Sequence alignment; Homology search
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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
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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
- G16B40/20—Supervised data analysis
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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
- G16B50/00—ICT programming tools or database systems specially adapted for bioinformatics
- G16B50/10—Ontologies; Annotations
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- 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)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Software Systems (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Molecular Biology (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Evolutionary Biology (AREA)
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- Biotechnology (AREA)
- General Engineering & Computer Science (AREA)
- Biomedical Technology (AREA)
- General Physics & Mathematics (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Computational Linguistics (AREA)
- Chemical & Material Sciences (AREA)
- Proteomics, Peptides & Aminoacids (AREA)
- Analytical Chemistry (AREA)
- Databases & Information Systems (AREA)
- Bioethics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Public Health (AREA)
- Epidemiology (AREA)
- Genetics & Genomics (AREA)
- Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
- Image Analysis (AREA)
- Electrically Operated Instructional Devices (AREA)
- Test And Diagnosis Of Digital Computers (AREA)
- Maintenance And Management Of Digital Transmission (AREA)
- Debugging And Monitoring (AREA)
- Feedback Control In General (AREA)
Abstract
La tecnología divulgada presenta un entorno basado en el aprendizaje profundo que identifica patrones de secuencia que causan errores específicos de secuencia (EES). Los sistemas y métodos entrenan un filtro de variantes con base en los datos de variantes a gran escala para reconocer dependencias causales entre los patrones de secuencia y las detecciones de variantes falsas. El filtro de variantes tiene una estructura jerárquica basada en redes neuronales profundas, como redes neuronales convolucionales y redes neuronales completamente conectadas. Los sistemas y métodos ejecutan una simulación que utiliza el filtro de variantes para evaluar el efecto de los patrones de secuencia conocidos en el filtrado de variantes. La premisa de la simulación es la siguiente: cuando un par de un patrón de repetición bajo evaluación y una variante detectada se alimentan al filtro de variantes como parte de una secuencia de entrada simulada y el filtro de variantes clasifica a la variante detectada como una detección de variante falsa, entonces se considera que el patrón de repetición ha causado la detección de variante falsa y lo identifica como causante de un EES.
Applications Claiming Priority (4)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US201862696699P | 2018-07-11 | 2018-07-11 | |
| NL2021473A NL2021473B1 (en) | 2018-07-11 | 2018-08-16 | DEEP LEARNING-BASED FRAMEWORK FOR IDENTIFYING SEQUENCE PATTERNS THAT CAUSE SEQUENCE-SPECIFIC ERRORS (SSEs) |
| US16/505,100 US12073922B2 (en) | 2018-07-11 | 2019-07-08 | Deep learning-based framework for identifying sequence patterns that cause sequence-specific errors (SSEs) |
| PCT/US2019/041078 WO2020014280A1 (en) | 2018-07-11 | 2019-07-09 | DEEP LEARNING-BASED FRAMEWORK FOR IDENTIFYING SEQUENCE PATTERNS THAT CAUSE SEQUENCE-SPECIFIC ERRORS (SSEs) |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| MX2019015567A MX2019015567A (es) | 2020-07-28 |
| MX393603B true MX393603B (es) | 2025-03-24 |
Family
ID=69183785
Family Applications (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| MX2019015567A MX393603B (es) | 2018-07-11 | 2019-07-09 | Entorno basado en el aprendizaje profundo para la identificacion de patrones de secuencia que provocan errores especificos de secuencia (ees) |
| MX2022008257A MX2022008257A (es) | 2018-07-11 | 2019-12-18 | Entorno basado en el aprendizaje profundo para la identificacion de patrones de secuencia que provocan errores especificos de secuencia (ees). |
Family Applications After (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| MX2022008257A MX2022008257A (es) | 2018-07-11 | 2019-12-18 | Entorno basado en el aprendizaje profundo para la identificacion de patrones de secuencia que provocan errores especificos de secuencia (ees). |
Country Status (14)
| Country | Link |
|---|---|
| EP (1) | EP3619712B1 (es) |
| JP (1) | JP6785995B2 (es) |
| KR (3) | KR102447812B1 (es) |
| CN (2) | CN118673964A (es) |
| AU (2) | AU2019272065C1 (es) |
| BR (1) | BR112019027637A2 (es) |
| CA (1) | CA3064226C (es) |
| IL (3) | IL296738B2 (es) |
| MX (2) | MX393603B (es) |
| MY (1) | MY204816A (es) |
| NZ (1) | NZ759884A (es) |
| RU (1) | RU2745733C1 (es) |
| SG (1) | SG11201912766VA (es) |
| ZA (1) | ZA201908149B (es) |
Families Citing this family (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111612157B (zh) * | 2020-05-22 | 2023-06-30 | 四川无声信息技术有限公司 | 训练方法、文字识别方法、装置、存储介质及电子设备 |
| CN111832717B (zh) * | 2020-06-24 | 2021-09-28 | 上海西井信息科技有限公司 | 芯片及用于卷积计算的处理装置 |
| AU2021327765B2 (en) * | 2020-08-21 | 2025-01-02 | Regeneron Pharmaceuticals, Inc. | Methods and systems for sequence generation and prediction |
| JP7574420B2 (ja) * | 2020-09-11 | 2024-10-28 | エフ． ホフマン－ラ ロシュ アーゲー | 多数のノイズのある配列からからコンセンサス配列を生成する深層学習ベースの技法 |
| CN113642826B (zh) * | 2021-06-02 | 2024-06-11 | 中国海洋大学 | 一种供应商违约风险预测方法 |
| CN113781551B (zh) * | 2021-09-06 | 2023-10-31 | 中南民族大学 | 基于视觉感知的茶园植物状态监测管理系统及其方法 |
| CN113656333B (zh) * | 2021-10-20 | 2022-03-18 | 之江实验室 | 一种加速深度学习训练任务数据载入的方法 |
| EP4222749B1 (en) * | 2021-12-24 | 2025-12-17 | GeneSense Technology Inc. | Deep learning based methods and systems for nucleic acid sequencing |
| CN114510993B (zh) * | 2021-12-28 | 2025-04-18 | 西安理工大学 | 基于sa-gru的高速列车节能驾驶策略 |
| CN114078073B (zh) * | 2022-01-20 | 2022-04-08 | 广州南方学院 | 一种基于光场转换的抗led再生拷贝方法及系统 |
| CN116131979A (zh) * | 2022-06-08 | 2023-05-16 | 上海前瞻创新研究院有限公司 | 受远程干扰的无线信道预测方法及系统、存储介质及终端 |
| CN119948569A (zh) * | 2022-07-06 | 2025-05-06 | 上海芯像生物科技有限公司 | 用于利用机器学习来增强高通量测序过程中的核酸测序质量的方法和系统 |
| CN115512779B (zh) * | 2022-09-24 | 2026-01-02 | 上海交通大学 | 重加权算法生成碱基特异性的核酸分子力场参数的方法 |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU2309457C1 (ru) * | 2006-05-06 | 2007-10-27 | Государственное общеобразовательное учреждение высшего профессионального образования "Уральский государственный технический университет-УПИ" | Модель нейронной сети |
| US9524369B2 (en) * | 2009-06-15 | 2016-12-20 | Complete Genomics, Inc. | Processing and analysis of complex nucleic acid sequence data |
| CN102597266A (zh) * | 2009-09-30 | 2012-07-18 | 纳特拉公司 | 无创性产前倍性调用的方法 |
| WO2013052907A2 (en) * | 2011-10-06 | 2013-04-11 | Sequenom, Inc. | Methods and processes for non-invasive assessment of genetic variations |
| US9367663B2 (en) * | 2011-10-06 | 2016-06-14 | Sequenom, Inc. | Methods and processes for non-invasive assessment of genetic variations |
| KR102028375B1 (ko) * | 2012-09-04 | 2019-10-04 | 가던트 헬쓰, 인크. | 희귀 돌연변이 및 카피수 변이를 검출하기 위한 시스템 및 방법 |
| US9406017B2 (en) * | 2012-12-24 | 2016-08-02 | Google Inc. | System and method for addressing overfitting in a neural network |
| US10068053B2 (en) * | 2013-12-16 | 2018-09-04 | Complete Genomics, Inc. | Basecaller for DNA sequencing using machine learning |
| CN107609351A (zh) * | 2017-10-23 | 2018-01-19 | 桂林电子科技大学 | 一种基于卷积神经网络预测假尿苷修饰位点的方法 |
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2019
- 2019-07-09 MY MYPI2019007299A patent/MY204816A/en unknown
- 2019-07-09 KR KR1020227007154A patent/KR102447812B1/ko active Active
- 2019-07-09 IL IL296738A patent/IL296738B2/en unknown
- 2019-07-09 NZ NZ759884A patent/NZ759884A/en not_active IP Right Cessation
- 2019-07-09 JP JP2019567519A patent/JP6785995B2/ja active Active
- 2019-07-09 BR BR112019027637-8A patent/BR112019027637A2/pt active Search and Examination
- 2019-07-09 MX MX2019015567A patent/MX393603B/es unknown
- 2019-07-09 CN CN202410602622.5A patent/CN118673964A/zh active Pending
- 2019-07-09 CA CA3064226A patent/CA3064226C/en active Active
- 2019-07-09 RU RU2019139413A patent/RU2745733C1/ru active
- 2019-07-09 SG SG11201912766VA patent/SG11201912766VA/en unknown
- 2019-07-09 CN CN201980003258.8A patent/CN110892484B/zh active Active
- 2019-07-09 AU AU2019272065A patent/AU2019272065C1/en not_active Ceased
- 2019-07-09 KR KR1020227032956A patent/KR102628141B1/ko active Active
- 2019-07-09 KR KR1020197036426A patent/KR102371706B1/ko not_active Expired - Fee Related
- 2019-07-09 EP EP19742664.6A patent/EP3619712B1/en active Active
- 2019-12-05 IL IL271213A patent/IL271213B/en unknown
- 2019-12-09 ZA ZA2019/08149A patent/ZA201908149B/en unknown
- 2019-12-18 MX MX2022008257A patent/MX2022008257A/es unknown
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2021
- 2021-05-31 AU AU2021203538A patent/AU2021203538B2/en not_active Ceased
- 2021-11-21 IL IL288276A patent/IL288276B2/en unknown
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