AU2020269607B2 - Identification of convergent antibody specificity sequence patterns - Google Patents
Identification of convergent antibody specificity sequence patternsInfo
- Publication number
- AU2020269607B2 AU2020269607B2 AU2020269607A AU2020269607A AU2020269607B2 AU 2020269607 B2 AU2020269607 B2 AU 2020269607B2 AU 2020269607 A AU2020269607 A AU 2020269607A AU 2020269607 A AU2020269607 A AU 2020269607A AU 2020269607 B2 AU2020269607 B2 AU 2020269607B2
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- AU
- Australia
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- amino acid
- sequence
- sequences
- convergent
- antigen
- Prior art date
- Legal status (The legal status 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 status listed.)
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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
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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
- G06N3/044—Recurrent networks, e.g. Hopfield 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
- G06N3/044—Recurrent networks, e.g. Hopfield networks
- G06N3/0442—Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
-
- 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
- G06N3/045—Combinations of networks
- G06N3/0455—Auto-encoder networks; Encoder-decoder 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
- G06N3/047—Probabilistic or stochastic networks
-
- 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
- G06N3/0475—Generative networks
-
- 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
- G06N3/088—Non-supervised learning, e.g. competitive learning
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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
- G06N3/0895—Weakly supervised learning, e.g. semi-supervised or self-supervised learning
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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
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computing arrangements based on specific mathematical models
- G06N7/01—Probabilistic graphical models, e.g. probabilistic 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
- 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
- G16B40/30—Unsupervised data analysis
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biophysics (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- General Physics & Mathematics (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Computational Linguistics (AREA)
- Biomedical Technology (AREA)
- Medical Informatics (AREA)
- Biotechnology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Evolutionary Biology (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Genetics & Genomics (AREA)
- Proteomics, Peptides & Aminoacids (AREA)
- Probability & Statistics with Applications (AREA)
- Computational Mathematics (AREA)
- Algebra (AREA)
- Mathematical Analysis (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Bioethics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Databases & Information Systems (AREA)
- Epidemiology (AREA)
- Public Health (AREA)
- Peptides Or Proteins (AREA)
Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US201962843010P | 2019-05-03 | 2019-05-03 | |
| US62/843,010 | 2019-05-03 | ||
| PCT/IB2020/054171 WO2020225693A1 (en) | 2019-05-03 | 2020-05-02 | Identification of convergent antibody specificity sequence patterns |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| AU2020269607A1 AU2020269607A1 (en) | 2021-10-28 |
| AU2020269607B2 true AU2020269607B2 (en) | 2025-12-11 |
Family
ID=70554146
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| AU2020269607A Active AU2020269607B2 (en) | 2019-05-03 | 2020-05-02 | Identification of convergent antibody specificity sequence patterns |
Country Status (8)
| Country | Link |
|---|---|
| US (1) | US20220164627A1 (https=) |
| EP (1) | EP3963590A1 (https=) |
| JP (2) | JP7602484B2 (https=) |
| CN (2) | CN114026645A (https=) |
| AU (1) | AU2020269607B2 (https=) |
| CA (1) | CA3132181A1 (https=) |
| IL (1) | IL287237A (https=) |
| WO (1) | WO2020225693A1 (https=) |
Families Citing this family (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP7648099B2 (ja) | 2019-05-19 | 2025-03-18 | ジャスト-エヴォテック バイオロジクス,インコーポレイテッド | 機械学習法によるタンパク質配列の生成 |
| CA3160429A1 (en) * | 2019-12-06 | 2021-06-10 | Philip M. KIM | System and method for generating a protein sequence |
| US11388356B1 (en) * | 2021-04-12 | 2022-07-12 | Tetramem Inc. | AI fusion pixel sensor using memristors |
| CN113393900B (zh) * | 2021-06-09 | 2022-08-02 | 吉林大学 | 基于改进Transformer模型的RNA状态推断研究方法 |
| US20250191674A1 (en) * | 2022-02-28 | 2025-06-12 | Genentech, Inc. | Protein design with segment preservation |
| US12587274B2 (en) | 2023-03-28 | 2026-03-24 | Quantum Generative Materials Llc | Satellite optimization management system based on natural language input and artificial intelligence |
| CN116895350B (zh) * | 2023-08-04 | 2024-01-16 | 辽宁工业大学 | 一种在复合位移加载下波纹管的多轴疲劳寿命预测方法 |
| WO2025074981A1 (ja) * | 2023-10-04 | 2025-04-10 | 国立大学法人大阪大学 | 抗体選別方法、コンピュータプログラム及び情報処理装置 |
| US12368503B2 (en) | 2023-12-27 | 2025-07-22 | Quantum Generative Materials Llc | Intent-based satellite transmit management based on preexisting historical location and machine learning |
| US12603701B2 (en) | 2023-12-27 | 2026-04-14 | Quantum Generative Materials Llc | Distributed satellite constellation management and control system |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8716195B2 (en) | 2005-11-14 | 2014-05-06 | Bioren, Inc. | Antibody ultrahumanization by predicted mature CDR blasting and cohort library generation and screening |
| US20230019590A1 (en) * | 2010-03-23 | 2023-01-19 | Iogenetics, Llc | Bioinformatic processes for determination of peptide binding |
| CN108138244A (zh) | 2015-09-18 | 2018-06-08 | 纽约市哥伦比亚大学理事会 | 病毒组捕获测序平台、设计和构建方法以及使用方法 |
| US11573239B2 (en) * | 2017-07-17 | 2023-02-07 | Bioinformatics Solutions Inc. | Methods and systems for de novo peptide sequencing using deep learning |
| EP3486816A1 (en) * | 2017-11-16 | 2019-05-22 | Institut Pasteur | Method, device, and computer program for generating protein sequences with autoregressive neural networks |
| CN108549794B (zh) * | 2018-03-29 | 2021-05-25 | 中国林业科学研究院资源昆虫研究所 | 一种蛋白质二级结构预测方法 |
| US20220180975A1 (en) * | 2019-01-28 | 2022-06-09 | The Broad Institute, Inc. | Methods and systems for determining gene expression profiles and cell identities from multi-omic imaging data |
-
2020
- 2020-05-02 JP JP2021561675A patent/JP7602484B2/ja active Active
- 2020-05-02 US US17/442,465 patent/US20220164627A1/en active Pending
- 2020-05-02 EP EP20724223.1A patent/EP3963590A1/en active Pending
- 2020-05-02 CA CA3132181A patent/CA3132181A1/en active Pending
- 2020-05-02 CN CN202080028478.9A patent/CN114026645A/zh active Pending
- 2020-05-02 CN CN202510594043.5A patent/CN120526845A/zh active Pending
- 2020-05-02 WO PCT/IB2020/054171 patent/WO2020225693A1/en not_active Ceased
- 2020-05-02 AU AU2020269607A patent/AU2020269607B2/en active Active
-
2021
- 2021-10-13 IL IL287237A patent/IL287237A/en unknown
-
2024
- 2024-09-10 JP JP2024155436A patent/JP2024167413A/ja active Pending
Non-Patent Citations (1)
| Title |
|---|
| PARAMESWARAN POORNIMA ET AL: "Convergent Antibody Signatures in Human Dengue", CELL HOST & MICROBE, ELSEVIER, NL, vol. 13, no. 6, 12 June 2013 (2013-06-12), pages 691 - 700, XP028568512, ISSN: 1931-3128, DOI: 10.1016/J.CHOM.2013.05.008 * |
Also Published As
| Publication number | Publication date |
|---|---|
| JP2024167413A (ja) | 2024-12-03 |
| IL287237A (en) | 2021-12-01 |
| US20220164627A1 (en) | 2022-05-26 |
| CN114026645A (zh) | 2022-02-08 |
| EP3963590A1 (en) | 2022-03-09 |
| JP2022530941A (ja) | 2022-07-05 |
| WO2020225693A1 (en) | 2020-11-12 |
| JP7602484B2 (ja) | 2024-12-18 |
| CN120526845A (zh) | 2025-08-22 |
| AU2020269607A1 (en) | 2021-10-28 |
| CA3132181A1 (en) | 2020-11-12 |
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Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| FGA | Letters patent sealed or granted (standard patent) |