MX2020008597A - Gan-cnn para la predicción de unión de péptidos al mhc. - Google Patents
Gan-cnn para la predicción de unión de péptidos al mhc.Info
- Publication number
- MX2020008597A MX2020008597A MX2020008597A MX2020008597A MX2020008597A MX 2020008597 A MX2020008597 A MX 2020008597A MX 2020008597 A MX2020008597 A MX 2020008597A MX 2020008597 A MX2020008597 A MX 2020008597A MX 2020008597 A MX2020008597 A MX 2020008597A
- Authority
- MX
- Mexico
- Prior art keywords
- cnn
- gan
- peptide binding
- mhc peptide
- binding prediction
- Prior art date
Links
- 108090000765 processed proteins & peptides Proteins 0.000 title abstract 2
- 238000013527 convolutional neural network Methods 0.000 abstract 4
- 238000000034 method Methods 0.000 abstract 2
- 230000006916 protein interaction Effects 0.000 abstract 2
- 229920001184 polypeptide Polymers 0.000 abstract 1
- 102000004196 processed proteins & peptides Human genes 0.000 abstract 1
- 230000002194 synthesizing effect Effects 0.000 abstract 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
- G16B20/30—Detection of binding sites or motifs
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- 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
- G06N3/0464—Convolutional networks [CNN, ConvNet]
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- 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
- G06N3/094—Adversarial 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
- 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
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/30—Prediction of properties of chemical compounds, compositions or mixtures
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/40—Searching chemical structures or physicochemical data
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/50—Molecular design, e.g. of drugs
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/70—Machine learning, data mining or chemometrics
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/90—Programming languages; Computing architectures; Database systems; Data warehousing
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C60/00—Computational materials science, i.e. ICT specially adapted for investigating the physical or chemical properties of materials or phenomena associated with their design, synthesis, processing, characterisation or utilisation
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C99/00—Subject matter not provided for in other groups of this subclass
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Computing Systems (AREA)
- Bioinformatics & Computational Biology (AREA)
- Chemical & Material Sciences (AREA)
- Health & Medical Sciences (AREA)
- Physics & Mathematics (AREA)
- Crystallography & Structural Chemistry (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Data Mining & Analysis (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Biophysics (AREA)
- Databases & Information Systems (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Biotechnology (AREA)
- Evolutionary Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Molecular Biology (AREA)
- Analytical Chemistry (AREA)
- Proteomics, Peptides & Aminoacids (AREA)
- Genetics & Genomics (AREA)
- Medicinal Chemistry (AREA)
- Pharmacology & Pharmacy (AREA)
- Public Health (AREA)
- Epidemiology (AREA)
- Bioethics (AREA)
- Biomedical Technology (AREA)
- Computational Linguistics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Investigating Or Analysing Biological Materials (AREA)
- Peptides Or Proteins (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
En la presente se divulgan métodos para entrenar una red neuronal generativa antagónica (GAN) conjuntamente con una red neuronal convolucional (CNN). La GAN y la CNN pueden entrenarse usando datos biológicos, como datos de interacción de proteínas. La CNN puede usarse para identificar nuevos datos como positivos o negativos. Se divulgan métodos para sintetizar un polipéptido asociado con nuevos datos de interacción de proteínas identificados como positivos.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US201862631710P | 2018-02-17 | 2018-02-17 | |
| PCT/US2019/018434 WO2019161342A1 (en) | 2018-02-17 | 2019-02-18 | Gan-cnn for mhc peptide binding prediction |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| MX2020008597A true MX2020008597A (es) | 2020-12-11 |
Family
ID=65686006
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| MX2020008597A MX2020008597A (es) | 2018-02-17 | 2019-02-18 | Gan-cnn para la predicción de unión de péptidos al mhc. |
Country Status (11)
| Country | Link |
|---|---|
| US (1) | US20190259474A1 (es) |
| EP (1) | EP3753022A1 (es) |
| JP (2) | JP7047115B2 (es) |
| KR (2) | KR102607567B1 (es) |
| CN (1) | CN112119464A (es) |
| AU (2) | AU2019221793B2 (es) |
| CA (1) | CA3091480A1 (es) |
| IL (2) | IL276730B1 (es) |
| MX (1) | MX2020008597A (es) |
| SG (1) | SG11202007854QA (es) |
| WO (1) | WO2019161342A1 (es) |
Families Citing this family (24)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB201718756D0 (en) * | 2017-11-13 | 2017-12-27 | Cambridge Bio-Augmentation Systems Ltd | Neural interface |
| US10706534B2 (en) * | 2017-07-26 | 2020-07-07 | Scott Anderson Middlebrooks | Method and apparatus for classifying a data point in imaging data |
| US11704573B2 (en) * | 2019-03-25 | 2023-07-18 | Here Global B.V. | Method, apparatus, and computer program product for identifying and compensating content contributors |
| US20200379814A1 (en) * | 2019-05-29 | 2020-12-03 | Advanced Micro Devices, Inc. | Computer resource scheduling using generative adversarial networks |
| AU2020290510A1 (en) * | 2019-06-12 | 2022-02-03 | Quantum-Si Incorporated | Techniques for protein identification using machine learning and related systems and methods |
| CN110598786B (zh) * | 2019-09-09 | 2022-01-07 | 京东方科技集团股份有限公司 | 神经网络的训练方法、语义分类方法、语义分类装置 |
| CN110875790A (zh) * | 2019-11-19 | 2020-03-10 | 上海大学 | 基于生成对抗网络的无线信道建模实现方法 |
| US20210150270A1 (en) * | 2019-11-19 | 2021-05-20 | International Business Machines Corporation | Mathematical function defined natural language annotation |
| EP4022500A1 (en) * | 2019-11-22 | 2022-07-06 | F. Hoffmann-La Roche AG | Multiple instance learner for tissue image classification |
| US20230005567A1 (en) * | 2019-12-12 | 2023-01-05 | Just- Evotec Biologics, Inc. | Generating protein sequences using machine learning techniques based on template protein sequences |
| CN111063391B (zh) * | 2019-12-20 | 2023-04-25 | 海南大学 | 一种基于生成式对抗网络原理的不可培养微生物筛选系统 |
| CN111402113B (zh) * | 2020-03-09 | 2021-10-15 | 北京字节跳动网络技术有限公司 | 图像处理方法、装置、电子设备及计算机可读介质 |
| WO2021195155A1 (en) * | 2020-03-23 | 2021-09-30 | Genentech, Inc. | Estimating pharmacokinetic parameters using deep learning |
| US20210295173A1 (en) * | 2020-03-23 | 2021-09-23 | Samsung Electronics Co., Ltd. | Method and apparatus for data-free network quantization and compression with adversarial knowledge distillation |
| US10885387B1 (en) * | 2020-08-04 | 2021-01-05 | SUPERB Al CO., LTD. | Methods for training auto-labeling device and performing auto-labeling by using hybrid classification and devices using the same |
| US10902291B1 (en) * | 2020-08-04 | 2021-01-26 | Superb Ai Co., Ltd. | Methods for training auto labeling device and performing auto labeling related to segmentation while performing automatic verification by using uncertainty scores and devices using the same |
| WO2022047150A1 (en) * | 2020-08-28 | 2022-03-03 | Just-Evotec Biologics, Inc. | Implementing a generative machine learning architecture to produce training data for a classification model |
| CN112597705B (zh) * | 2020-12-28 | 2022-05-24 | 哈尔滨工业大学 | 一种基于scvnn的多特征健康因子融合方法 |
| CN112309497B (zh) * | 2020-12-28 | 2021-04-02 | 武汉金开瑞生物工程有限公司 | 一种基于Cycle-GAN的蛋白质结构预测方法及装置 |
| KR102519341B1 (ko) * | 2021-03-18 | 2023-04-06 | 재단법인한국조선해양기자재연구원 | 소음분석을 통한 타이어 편마모 조기 감지 시스템 및 그 방법 |
| US20220328127A1 (en) * | 2021-04-05 | 2022-10-13 | Nec Laboratories America, Inc. | Peptide based vaccine generation system with dual projection generative adversarial networks |
| US20220319635A1 (en) * | 2021-04-05 | 2022-10-06 | Nec Laboratories America, Inc. | Generating minority-class examples for training data |
| US20230083313A1 (en) * | 2021-09-13 | 2023-03-16 | Nec Laboratories America, Inc. | Peptide search system for immunotherapy |
| KR102507111B1 (ko) * | 2022-03-29 | 2023-03-07 | 주식회사 네오젠티씨 | 데이터베이스에 저장된 면역 펩티돔 정보의 신뢰도를 결정하기 위한 방법 및 장치 |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8121797B2 (en) * | 2007-01-12 | 2012-02-21 | Microsoft Corporation | T-cell epitope prediction |
| US9805305B2 (en) * | 2015-08-07 | 2017-10-31 | Yahoo Holdings, Inc. | Boosted deep convolutional neural networks (CNNs) |
| WO2018022752A1 (en) | 2016-07-27 | 2018-02-01 | James R. Glidewell Dental Ceramics, Inc. | Dental cad automation using deep learning |
| CN106845471A (zh) * | 2017-02-20 | 2017-06-13 | 深圳市唯特视科技有限公司 | 一种基于生成对抗网络的视觉显著性预测方法 |
| CN107590518A (zh) * | 2017-08-14 | 2018-01-16 | 华南理工大学 | 一种多特征学习的对抗网络训练方法 |
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2019
- 2019-02-18 EP EP19709215.8A patent/EP3753022A1/en active Pending
- 2019-02-18 CN CN201980025487.XA patent/CN112119464A/zh active Pending
- 2019-02-18 MX MX2020008597A patent/MX2020008597A/es unknown
- 2019-02-18 SG SG11202007854QA patent/SG11202007854QA/en unknown
- 2019-02-18 IL IL276730A patent/IL276730B1/en unknown
- 2019-02-18 IL IL311528A patent/IL311528A/en unknown
- 2019-02-18 KR KR1020207026559A patent/KR102607567B1/ko active Application Filing
- 2019-02-18 AU AU2019221793A patent/AU2019221793B2/en active Active
- 2019-02-18 KR KR1020237040230A patent/KR20230164757A/ko active Search and Examination
- 2019-02-18 WO PCT/US2019/018434 patent/WO2019161342A1/en active Application Filing
- 2019-02-18 JP JP2020543800A patent/JP7047115B2/ja active Active
- 2019-02-18 US US16/278,611 patent/US20190259474A1/en active Pending
- 2019-02-18 CA CA3091480A patent/CA3091480A1/en active Pending
-
2022
- 2022-03-23 JP JP2022046973A patent/JP7459159B2/ja active Active
- 2022-08-26 AU AU2022221568A patent/AU2022221568B2/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| IL311528A (en) | 2024-05-01 |
| US20190259474A1 (en) | 2019-08-22 |
| SG11202007854QA (en) | 2020-09-29 |
| AU2022221568B2 (en) | 2024-06-13 |
| AU2019221793B2 (en) | 2022-09-15 |
| CN112119464A (zh) | 2020-12-22 |
| WO2019161342A1 (en) | 2019-08-22 |
| CA3091480A1 (en) | 2019-08-22 |
| KR20230164757A (ko) | 2023-12-04 |
| RU2020130420A3 (es) | 2022-03-17 |
| IL276730A (en) | 2020-09-30 |
| AU2019221793A1 (en) | 2020-09-17 |
| AU2022221568A1 (en) | 2022-09-22 |
| JP2021514086A (ja) | 2021-06-03 |
| JP7047115B2 (ja) | 2022-04-04 |
| EP3753022A1 (en) | 2020-12-23 |
| RU2020130420A (ru) | 2022-03-17 |
| KR20200125948A (ko) | 2020-11-05 |
| IL276730B1 (en) | 2024-04-01 |
| JP7459159B2 (ja) | 2024-04-01 |
| JP2022101551A (ja) | 2022-07-06 |
| KR102607567B1 (ko) | 2023-12-01 |
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