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  Artificial intelligence for proteomics and biomarker discovery

Mann, M., Kumar, C., Zeng, W.-F., & Strauss, M. T. (2021). Artificial intelligence for proteomics and biomarker discovery. Cell Systems, 12(8), 759-770. doi:10.1016/j.cels.2021.06.006.

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Genre: Journal Article

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 Creators:
Mann, M.1, Author              
Kumar, Chanchal2, Author
Zeng, Wen-Feng1, Author
Strauss, Maximilian T.2, Author
Affiliations:
1Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society, ou_1565159              
2external, ou_persistent22              

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Free keywords: TANDEM MASS-SPECTRA; PROTEIN; PREDICTION; PEPTIDES; IDENTIFICATIONBiochemistry & Molecular Biology; Cell Biology;
 Abstract: 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.

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Language(s): eng - English
 Dates: 2021
 Publication Status: Published in print
 Pages: 12
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Degree: -

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Title: Cell Systems
Source Genre: Journal
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Publ. Info: Maryland Heights, MO : Elsevier
Pages: - Volume / Issue: 12 (8) Sequence Number: - Start / End Page: 759 - 770 Identifier: ISSN: 2405-4720
CoNE: https://pure.mpg.de/cone/journals/resource/2405-4720