IL307667A - Efficient voxelization for deep learning - Google Patents
Efficient voxelization for deep learningInfo
- Publication number
- IL307667A IL307667A IL307667A IL30766723A IL307667A IL 307667 A IL307667 A IL 307667A IL 307667 A IL307667 A IL 307667A IL 30766723 A IL30766723 A IL 30766723A IL 307667 A IL307667 A IL 307667A
- Authority
- IL
- Israel
- Prior art keywords
- cell
- atoms
- coordinates
- mapping
- voxels
- Prior art date
Links
- 238000013135 deep learning Methods 0.000 title 1
- 125000004429 atom Chemical group 0.000 claims 32
- 238000013507 mapping Methods 0.000 claims 21
- 238000000034 method Methods 0.000 claims 19
- 102000004169 proteins and genes Human genes 0.000 claims 11
- 108090000623 proteins and genes Proteins 0.000 claims 11
- 150000001413 amino acids Chemical class 0.000 claims 5
- 125000004432 carbon atom Chemical group C* 0.000 claims 3
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
-
- 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
- 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
- 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)
Claims (20)
1.Claims 1. A computer-implemented method of efficiently determining which elements of a sequence are nearest to uniformly spaced cells in a grid, wherein the elements have element coordinates, and cells have dimension-wise cell indices and cell coordinates, the computer- implemented method comprising: generating an element-to-cells mapping that maps, to each of the elements, a subset of the cells, wherein the subset of the cells mapped to a particular element in the sequence includes a nearest cell in the grid and one or more neighborhood cells in the grid, wherein the nearest cell is selected based on matching element coordinates of the particular element to the cell coordinates, and wherein the one or more neighborhood cells are contiguously adjacent to the nearest cell and selected based on being within a distance proximity range from the particular element; generating a cell-to-elements mapping that maps, to each of the cells, a subset of the elements, wherein the subset of the elements mapped to a particular cell in the grid includes those elements in the sequence that are mapped to the particular cell by the element-to-cells mapping; and using the cell-to-elements mapping to determine, for each of the cells, a nearest element in the sequence, wherein the nearest element to the particular cell is determined based on distances between the particular cell and the elements in the subset of the elements.
2. The computer-implemented method of claim 1, wherein matching the element coordinates of the particular element to the cell coordinates further includes truncating a decimal portion of the element coordinates to generate truncated element coordinates.
3. The computer-implemented method of claim 1 or 2, wherein matching the element coordinates of the particular element to the cell coordinates further includes: for a first dimension, matching a first truncated element coordinate in the truncated element coordinates to a first cell coordinate of a first cell in the grid, and selecting a first dimension index of the first cell; for a second dimension, matching a second truncated element coordinate in the truncated element coordinates to a second cell coordinate of a second cell in the grid, and selecting a second dimension index of the second cell; for a third dimension, matching a third truncated element coordinate in the truncated element coordinates to a third cell coordinate of a third cell in the grid, and selecting a third dimension index of the third cell; using the selected first dimension index, the selected second dimension index, and the selected third dimension index to generate an accumulated sum based on position-wise weighting the selected first dimension index, the selected second dimension index, and the selected third dimension index by powers of a radix; and using the accumulated sum as a cell index for selection of the nearest cell.
4. The computer-implemented method of any of claims 1-3, wherein the distances are calculated between cell coordinates of the particular cell and the element coordinates of the elements in the subset of the elements.
5. The computer-implemented method of any of claims 1-4, wherein the sequence is a protein sequence of amino acids.
6. The computer-implemented method of claim 5, wherein the elements are atoms of the amino acids from the protein sequence of amino acids.
7. The computer-implemented method of claim 6, wherein generating the element-to- cells mapping, generating the cell-to-elements mapping, and using the cell-to-elements mapping to determine, for each of the cells, the nearest element have a runtime complexity of O(a * f + v), wherein: a is a number of the atoms of the amino acids, f is a number of the amino acids, v is a number of the cells, and * is a multiplication operation.
8. The computer-implemented method of claim 6 or 7, wherein the atoms include alpha carbon atoms.
9. The computer-implemented method of any of claims 6-8, wherein the atoms include beta carbon atoms.
10. The computer-implemented method of any of claims 6-9, wherein the atoms include non-carbon atoms.
11. The computer-implemented method of any of claims 1-10, wherein the cells are three-dimensional voxels.
12. The computer-implemented method of any of claims 1-11, wherein the cell coordinates are three-dimensional coordinates.
13. The computer-implemented method of any of claims 1-12, wherein the element coordinates are three-dimensional coordinates.
14. The computer-implemented method of any of claim 1-13, wherein the one or more neighborhood cells are selected based on being within an index adjacency range from the nearest cell.
15. The computer-implemented method of any of claims 1-14, wherein the one or more neighborhood cells are selected based on being within a cell neighborhood in the grid that includes the nearest cell.
16. The computer-implemented method of any of claims 1-15, wherein the sequence includes M elements, wherein the subset of the elements includes N elements, and wherein M > > N.
17. A computer-implemented method of efficiently determining which atoms in a protein are nearest to voxels in a grid, wherein the atoms have three-dimensional (3D) atom coordinates, and the voxels have 3D voxel coordinates, including: generating an atom-to-voxels mapping that maps, to each of the atoms, a containing voxel selected based on matching 3D atom coordinates of a particular atom of the protein to the 3D voxel coordinates in the grid; generating a voxel-to-atoms mapping that maps, to each of the voxels, a subset of the atoms, wherein the subset of the atoms mapped to a particular voxel in the grid includes those atoms in the protein that are mapped to the particular voxel by the atom-to-voxels mapping; and using the voxel-to-atoms mapping to determine, for each of the voxels, a nearest atom in the protein.
18. The computer-implemented method of claim 17, wherein generating the atom-to- voxels mapping, generating the atom-to-voxels mapping, and using the voxel-to-atoms mapping to determine, for each of the voxels, the nearest atom have a runtime complexity of O(number of atoms).
19. A system for efficiently determining which atoms in a protein are nearest to voxels in a grid, wherein the atoms have three-dimensional (3D) atom coordinates, and the voxels have 3D voxel coordinates, the system including: at least a processor; and a non-transitory computer readable storage medium storing instructions that, when executed by at least the processor, cause the system to: generate an atom-to-voxels mapping that maps, to each of the atoms, a containing voxel selected based on matching 3D atom coordinates of a particular atom of the protein to the 3D voxel coordinates in the grid; generate a voxel-to-atoms mapping that maps, to each of the voxels, a subset of the atoms, wherein the subset of the atoms mapped to a particular voxel in the grid includes those atoms in the protein that are mapped to the particular voxel by the atom-to-voxels mapping; and use the voxel-to-atoms mapping to determine, for each of the voxels, a nearest atom in the protein.
20. The system of claim 19, further storing instructions that, when executed by at least the processor, cause the system to: generate the atom-to-voxels mapping using a runtime complexity determined by a number of the atoms; generate the voxel-to-atoms mapping using the runtime complexity determined by the number of the atoms; and use the voxel-to-atoms mapping to determine, for each of the voxels, the nearest atom in the protein based on distances between each voxel of the voxels and each atom in the subset of the atoms.
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/US2022/024918 WO2022221593A1 (en) | 2021-04-15 | 2022-04-14 | Efficient voxelization for deep learning |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| IL307667A true IL307667A (en) | 2023-12-01 |
Family
ID=81448684
Family Applications (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| IL307661A IL307661A (en) | 2021-04-15 | 2022-04-14 | Multi-channel protein voxelization to predict variant pathogenicity using deep convolutional neural networks |
| IL307667A IL307667A (en) | 2021-04-15 | 2022-04-14 | Efficient voxelization for deep learning |
Family Applications Before (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| IL307661A IL307661A (en) | 2021-04-15 | 2022-04-14 | Multi-channel protein voxelization to predict variant pathogenicity using deep convolutional neural networks |
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 |
| WO2022221591A1 (en) | 2022-10-20 |
| EP4323989A1 (en) | 2024-02-21 |
| JP2024513995A (en) | 2024-03-27 |
| KR20230170680A (en) | 2023-12-19 |
| BR112023021266A2 (en) | 2023-12-12 |
| 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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