Methods of profiling mass spectral data using neural networks
US11587644B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 30, 2018 |
| Grant date | Feb 21, 2023 |
| Priority date | — |
| Expiry date | Jun 21, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/04
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Methods are provided to classify and identify features in mass spectral data using neural network algorithms. A convolutional neural network (CNN) was trained to identify amino acids from an unknown protein sample. The CNN was trained using known peptide sequences to predict amino acid presence, diversity, and frequency, peptide length, subsequences of amino acids classified by features include aliphatic/aromatic, hydrophobic/hydrophilic, positive/negative charge, and combinations thereof. Mass spectra data of a sample unknown to the trained CNN was discretized into a one-dimensional vector and input into the CNN. The CNN models can potentially be integrated to determine the complete peptide sequence from a spectrum, thereby improving the yield of identifiable protein sequences from mass spec analysis.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.