Update in Proteomics mass spectrometry #1029
Description
Many papers on mass-spectrometry based proteomics have shown the use of deep learning for intensity prediction, combining scores across search algorithms, retention time, collision corss section prediction etc. It is an exciting area that may be covered in the v2.0 of the draft. Some of the papers and reviews (not comprehensive) are listed below-
- @doi:10.1016/j.crmeth.2021.100003 (Deep learning neural network tools for proteomics)
- @doi: 10.1074/mcp.TIR119.001412 (Prediction of LC-MS/MS Properties of Peptides from Sequence by Deep Learning)
- @doi: 10.1038/s41467-019-13866-z (In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics)
- @doi: 10.1080/14789450.2021.2020654 (Deep learning approaches for data-independent acquisition proteomics)
- @doi: 10.1093/bioinformatics/btab311 (On the feasibility of deep learning applications using raw mass spectrometry data)
- @doi: 10.1038/s41592-018-0260-3 (Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry)
- @doi: 10.1038/nbt.4313 (Deep learning using tumor HLA peptide mass spectrometry datasets improves neoantigen identification)
- @doi: 10.1038/s41592-019-0426-7 (Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning)
- @doi: 10.1093/bioinformatics/btx724 (Deep learning for tumor classification in imaging mass spectrometry)
- @doi: 10.1002/pmic.201900334 (DeepRescore: Leveraging Deep Learning to Improve Peptide Identification in Immunopeptidomics)
- @doi: 10.1002/pmic.202000009 (Using Deep Learning to Extrapolate Protein Expression Measurements)
- @doi: 10.1007/978-3-030-29726-8_19 (Book chapter- Deep Learning for Proteomics Data for Feature Selection and Classification)
- @doi:10.1002/pmic.201900335 (review- Deep Learning in Proteomics)