BR112023021266A2 - MULTI-CHANNEL PROTEIN VOXELIZATION TO PREDICT VARIANT PATHOGENICITY USING DEEP CONVOLUTIONAL NEURAL NETWORKS - Google Patents
MULTI-CHANNEL PROTEIN VOXELIZATION TO PREDICT VARIANT PATHOGENICITY USING DEEP CONVOLUTIONAL NEURAL NETWORKSInfo
- Publication number
- BR112023021266A2 BR112023021266A2 BR112023021266A BR112023021266A BR112023021266A2 BR 112023021266 A2 BR112023021266 A2 BR 112023021266A2 BR 112023021266 A BR112023021266 A BR 112023021266A BR 112023021266 A BR112023021266 A BR 112023021266A BR 112023021266 A2 BR112023021266 A2 BR 112023021266A2
- Authority
- BR
- Brazil
- Prior art keywords
- convolutional neural
- dimensional
- sequence
- evolutionary conservation
- alternative allele
- Prior art date
Links
- 238000013527 convolutional neural network Methods 0.000 title abstract 3
- 230000007918 pathogenicity Effects 0.000 title abstract 2
- 108090000623 proteins and genes Proteins 0.000 title abstract 2
- 102000004169 proteins and genes Human genes 0.000 title abstract 2
- 108700028369 Alleles Proteins 0.000 abstract 4
- 125000003275 alpha amino acid group Chemical group 0.000 abstract 2
- 150000001413 amino acids Chemical class 0.000 abstract 2
- 239000002773 nucleotide Substances 0.000 abstract 1
- 125000003729 nucleotide group Chemical group 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
- G16B15/00—ICT specially adapted for analysing two-dimensional or three-dimensional molecular structures, e.g. structural or functional relations or structure alignment
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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
- G06N3/045—Combinations of networks
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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
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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
-
- 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
- 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
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
Landscapes
- Engineering & Computer Science (AREA)
- 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)
- Bioinformatics & Cheminformatics (AREA)
- Medical Informatics (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Evolutionary Computation (AREA)
- Molecular Biology (AREA)
- Artificial Intelligence (AREA)
- Software Systems (AREA)
- Bioinformatics & Computational Biology (AREA)
- Biotechnology (AREA)
- Evolutionary Biology (AREA)
- Computing Systems (AREA)
- Biomedical Technology (AREA)
- Mathematical Physics (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Computational Linguistics (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Proteomics, Peptides & Aminoacids (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Genetics & Genomics (AREA)
- Databases & Information Systems (AREA)
- Bioethics (AREA)
- Public Health (AREA)
- Crystallography & Structural Chemistry (AREA)
- Epidemiology (AREA)
- Investigating Or Analysing Biological Materials (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Image Processing (AREA)
- Image Generation (AREA)
- Magnetic Resonance Imaging Apparatus (AREA)
Abstract
trata-se de um sistema que inclui pelo menos um voxelizador, um codificador de alelo alternativo, um codificador de conservação evolucionária e uma rede neural convolucional. o voxelizador acessa uma estrutura tridimensional de uma sequência de aminoácidos de referência de uma proteína e encaixa uma grade tridimensional de voxels em átomos na estrutura tridimensional por aminoácidos para gerar canais de distância em aminoácidos. o codificador de alelo alternativo codifica uma sequência de alelos alternativa para cada voxel na grade tridimensional de voxels. o codificador de conservação evolucionária codifica uma sequência de conservação evolucionária para cada voxel na grade tridimensional de voxels. a rede neural convolucional aplica convoluções tridimensionais a um tensor que inclui os canais de distância em aminoácidos codificados com a sequência de alelos alternativa e as respectivas sequências de conservação evolucionárias, e determina uma patogenicidade de um nucleotídeo variante com base, pelo menos em parte, no tensor.it is a system that includes at least a voxelizer, an alternative allele encoder, an evolutionary conservation encoder and a convolutional neural network. The voxelizer accesses a three-dimensional structure of a reference amino acid sequence of a protein and fits a three-dimensional grid of voxels into atoms into the three-dimensional amino acid structure to generate distance channels in amino acids. the alternative allele encoder encodes an alternative allele sequence for each voxel in the three-dimensional grid of voxels. the evolutionary conservation encoder encodes an evolutionary conservation sequence for each voxel in the three-dimensional grid of voxels. The convolutional neural network applies three-dimensional convolutions to a tensor that includes the distance channels in amino acids encoded with the alternative allele sequence and the respective evolutionary conservation sequences, and determines a pathogenicity of a nucleotide variant based, at least in part, on the tensioner.
Applications Claiming Priority (5)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US202163175495P | 2021-04-15 | 2021-04-15 | |
| US202163175767P | 2021-04-16 | 2021-04-16 | |
| US17/703,935 US20220336056A1 (en) | 2021-04-15 | 2022-03-24 | Multi-channel protein voxelization to predict variant pathogenicity using deep convolutional neural networks |
| US17/703,958 US20220336057A1 (en) | 2021-04-15 | 2022-03-24 | Efficient voxelization for deep learning |
| PCT/US2022/024916 WO2022221591A1 (en) | 2021-04-15 | 2022-04-14 | Multi-channel protein voxelization to predict variant pathogenicity using deep convolutional neural networks |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| BR112023021266A2 true BR112023021266A2 (en) | 2023-12-12 |
Family
ID=81448684
Family Applications (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| BR112023021266A BR112023021266A2 (en) | 2021-04-15 | 2022-04-14 | MULTI-CHANNEL PROTEIN VOXELIZATION TO PREDICT VARIANT PATHOGENICITY USING DEEP CONVOLUTIONAL NEURAL NETWORKS |
| BR112023021343A BR112023021343A2 (en) | 2021-04-15 | 2022-04-14 | EFFICIENT VOXELIZATION FOR DEEP LEARNING |
Family Applications After (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| BR112023021343A BR112023021343A2 (en) | 2021-04-15 | 2022-04-14 | EFFICIENT VOXELIZATION FOR DEEP LEARNING |
Country Status (9)
| Country | Link |
|---|---|
| EP (2) | EP4323991A1 (en) |
| JP (2) | JP2024514894A (en) |
| KR (2) | KR20230170680A (en) |
| AU (2) | AU2022258691A1 (en) |
| BR (2) | BR112023021266A2 (en) |
| CA (2) | CA3215514A1 (en) |
| IL (2) | IL307661A (en) |
| MX (2) | MX2023012226A (en) |
| WO (2) | WO2022221593A1 (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116153404B (en) * | 2023-02-28 | 2023-08-15 | 成都信息工程大学 | Single-cell ATAC-seq data analysis method |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3622521A1 (en) * | 2017-10-16 | 2020-03-18 | Illumina, Inc. | Deep convolutional neural networks for variant classification |
| EP3704640A4 (en) * | 2017-10-27 | 2021-08-18 | Apostle, Inc. | Predicting cancer-related pathogenic impact of somatic mutations using deep learning-based methods |
| CN110245685B (en) * | 2019-05-15 | 2022-03-25 | 清华大学 | Method, system and storage medium for predicting pathogenicity of genome single-site variation |
-
2022
- 2022-04-14 KR KR1020237034825A patent/KR20230170680A/en unknown
- 2022-04-14 WO PCT/US2022/024918 patent/WO2022221593A1/en active Application Filing
- 2022-04-14 CA CA3215514A patent/CA3215514A1/en active Pending
- 2022-04-14 AU AU2022258691A patent/AU2022258691A1/en active Pending
- 2022-04-14 AU AU2022259667A patent/AU2022259667A1/en active Pending
- 2022-04-14 JP JP2023563036A patent/JP2024514894A/en active Pending
- 2022-04-14 JP JP2023563033A patent/JP2024513995A/en active Pending
- 2022-04-14 KR KR1020237034824A patent/KR20230170679A/en unknown
- 2022-04-14 WO PCT/US2022/024916 patent/WO2022221591A1/en active Application Filing
- 2022-04-14 IL IL307661A patent/IL307661A/en unknown
- 2022-04-14 IL IL307667A patent/IL307667A/en unknown
- 2022-04-14 MX MX2023012226A patent/MX2023012226A/en unknown
- 2022-04-14 EP EP22726207.8A patent/EP4323991A1/en active Pending
- 2022-04-14 BR BR112023021266A patent/BR112023021266A2/en unknown
- 2022-04-14 MX MX2023012227A patent/MX2023012227A/en unknown
- 2022-04-14 EP EP22720250.4A patent/EP4323989A1/en active Pending
- 2022-04-14 BR BR112023021343A patent/BR112023021343A2/en unknown
- 2022-04-14 CA CA3215520A patent/CA3215520A1/en active Pending
Also Published As
| Publication number | Publication date |
|---|---|
| CA3215520A1 (en) | 2022-10-20 |
| EP4323991A1 (en) | 2024-02-21 |
| JP2024514894A (en) | 2024-04-03 |
| WO2022221593A1 (en) | 2022-10-20 |
| MX2023012227A (en) | 2024-01-08 |
| MX2023012226A (en) | 2024-01-08 |
| CA3215514A1 (en) | 2022-10-20 |
| IL307667A (en) | 2023-12-01 |
| WO2022221591A1 (en) | 2022-10-20 |
| EP4323989A1 (en) | 2024-02-21 |
| JP2024513995A (en) | 2024-03-27 |
| KR20230170680A (en) | 2023-12-19 |
| KR20230170679A (en) | 2023-12-19 |
| BR112023021343A2 (en) | 2023-12-19 |
| AU2022259667A1 (en) | 2023-10-26 |
| AU2022258691A1 (en) | 2023-10-26 |
| IL307661A (en) | 2023-12-01 |
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