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

Furkan M. Torun, Sebastian Virreira Winter, Sophia Doll, Felix M. Riese, Artem Vorobyev, View ORCID ProfileJohannes B. Mueller-Reif, View ORCID ProfilePhilipp E. Geyer, Maximilian T. Strauss
doi: https://doi.org/10.1101/2021.03.05.434053
Furkan M. Torun
1OmicEra Diagnostics GmbH, Planegg, Germany
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Sebastian Virreira Winter
1OmicEra Diagnostics GmbH, Planegg, Germany
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Sophia Doll
1OmicEra Diagnostics GmbH, Planegg, Germany
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Felix M. Riese
1OmicEra Diagnostics GmbH, Planegg, Germany
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Artem Vorobyev
1OmicEra Diagnostics GmbH, Planegg, Germany
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Johannes B. Mueller-Reif
1OmicEra Diagnostics GmbH, Planegg, Germany
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  • ORCID record for Johannes B. Mueller-Reif
Philipp E. Geyer
1OmicEra Diagnostics GmbH, Planegg, Germany
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  • ORCID record for Philipp E. Geyer
Maximilian T. Strauss
1OmicEra Diagnostics GmbH, Planegg, Germany
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  • For correspondence: Strauss@OmicEra.com
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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.

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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

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted March 06, 2021.
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Transparent exploration of machine learning for biomarker discovery from proteomics and omics data
Furkan M. Torun, Sebastian Virreira Winter, Sophia Doll, Felix M. Riese, Artem Vorobyev, Johannes B. Mueller-Reif, Philipp E. Geyer, Maximilian T. Strauss
bioRxiv 2021.03.05.434053; doi: https://doi.org/10.1101/2021.03.05.434053
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Transparent exploration of machine learning for biomarker discovery from proteomics and omics data
Furkan M. Torun, Sebastian Virreira Winter, Sophia Doll, Felix M. Riese, Artem Vorobyev, Johannes B. Mueller-Reif, Philipp E. Geyer, Maximilian T. Strauss
bioRxiv 2021.03.05.434053; doi: https://doi.org/10.1101/2021.03.05.434053

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