RT Journal Article
SR Electronic
T1 Transparent exploration of machine learning for biomarker discovery from proteomics and omics data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.03.05.434053
DO 10.1101/2021.03.05.434053
A1 Torun, Furkan M.
A1 Winter, Sebastian Virreira
A1 Doll, Sophia
A1 Riese, Felix M.
A1 Vorobyev, Artem
A1 Mueller-Reif, Johannes B.
A1 Geyer, Philipp E.
A1 Strauss, Maximilian T.
YR 2021
UL http://biorxiv.org/content/early/2021/03/06/2021.03.05.434053.abstract
AB Biomarkers are of central importance for assessing the health state and to guide medical interventions and their efficacy, but 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 indispensable for this purpose, however, it is sometimes applied in an opaque manner, generally requires expert knowledge and complex and expensive software. To enable easy access to ML for biomarker discovery without any programming or bioinformatic skills, we developed ‘OmicLearn’ (https://OmicLearn.com), an open-source web-based ML tool using the latest advances in the Python ML ecosystem. We host a web server for the exploration of the researcher’s results that can readily be cloned for internal use. Output tables from proteomics experiments are easily uploaded to the central or a local webserver. OmicLearn enables rapid exploration of the suitability of various ML algorithms for the experimental datasets. It fosters open science via transparent assessment of state-of-the-art algorithms in a standardized format for proteomics and other omics sciences.HighlightsOmicLearn is an open-source platform allows researchers to apply machine learning (ML) for biomarker discoveryThe ready-to-use structure of OmicLearn enables accessing state-of-the-art ML algorithms without requiring any prior bioinformatics knowledgeOmicLearn’s web-based interface provides an easy-to-follow platform for classification and gaining insights into the datasetSeveral algorithms and methods for preprocessing, feature selection, classification and cross-validation of omics datasets are integratedAll results, settings and method text can be exported in publication-ready formats