Transparent Exploration of Machine Learning for Biomarker Discovery from Proteomics and Omics Data
Research output: Contribution to journal › Journal article › Research › peer-review
Documents
- Fulltext
Final published version, 2.15 MB, PDF document
Biomarkers are of central importance for assessing the health state and to guide medical interventions and their efficacy; still, they are lacking for most diseases. Mass spectrometry (MS)-based proteomics is a powerful technology for biomarker discovery but requires sophisticated bioinformatics to identify robust patterns. Machine learning (ML) has become a promising tool for this purpose. However, it is sometimes applied in an opaque manner and generally requires specialized knowledge. To enable easy access to ML for biomarker discovery without any programming or bioinformatics skills, we developed "OmicLearn"(http://OmicLearn.org), an open-source browser-based ML tool using the latest advances in the Python ML ecosystem. Data matrices from omics experiments are easily uploaded to an online or a locally installed web server. OmicLearn enables rapid exploration of the suitability of various ML algorithms for the experimental data sets. It fosters open science via transparent assessment of state-of-the-art algorithms in a standardized format for proteomics and other omics sciences.
Original language | English |
---|---|
Journal | Journal of Proteome Research |
Volume | 22 |
Issue number | 2 |
Pages (from-to) | 359-367 |
ISSN | 1535-3893 |
DOIs | |
Publication status | Published - 2023 |
Bibliographical note
Publisher Copyright:
© 2022 The Authors. Published by American Chemical Society.
- diagnostics, machine learning, mass spectrometry, metabolome, omics, proteome, transcriptome
Research areas
ID: 329284339