Transparent exploration of machine learning for biomarker discovery from proteomics and omics data

Abstract 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. Graphical Abstract Highlights OmicLearn is an open-source platform allows researchers to apply machine learning (ML) for biomarker discovery The ready-to-use structure of OmicLearn enables accessing state-of-the-art ML algorithms without requiring any prior bioinformatics knowledge OmicLearn’s web-based interface provides an easy-to-follow platform for classification and gaining insights into the dataset Several algorithms and methods for preprocessing, feature selection, classification and cross-validation of omics datasets are integrated All results, settings and method text can be exported in publication-ready formats