Artificial intelligence for proteomics and biomarker discovery.

By Matthias Mann, Chanchal Kumar, Wen-feng Zeng, Maximilian T. Strauss. Cell Systems, 2021

301 citations recorded by the source corpus.

There is an avalanche of biomedical data generation and a parallel expansion in computational capabilities to analyze and make sense of these data. Starting with genome sequencing and widely employed deep sequencing technologies, these trends have now taken hold in all omics disciplines and increasingly call for multi-omics integration as well as data interpretation by artificial intelligence technologies. Here, we focus on mass spectrometry (MS)-based proteomics and describe how machine learning and, in particular, deep learning now predicts experimental peptide measurements from amino acid sequences alone. This will dramatically improve the quality and reliability of analytical workflows because experimental results should agree with predictions in a multi-dimensional data landscape. Machine learning has also become central to biomarker discovery from proteomics data, which now starts to outperform existing best-in-class assays. Finally, we discuss model transparency and explainability and data privacy that are required to deploy MS-based biomarkers in clinical settings.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/a5395bfa829b66091b92c362781df3087727f9ef

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The ProteomeXchange consortium at 10 years: 2023 update (2022, background): “the prediction of protein digestion, peptide retention time, peptide fragmentation, collision cross-section for ion mobility and/or improvements in algorithms for peptide and protein identification and quantification (for a recent review, see (53)), among other applications.”