Transparent Exploration of Machine Learning for Biomarker Discovery from Proteomics and Omics Data
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Transparent Exploration of Machine Learning for Biomarker Discovery from Proteomics and Omics Data. / Torun, Furkan M.; Virreira Winter, Sebastian; Doll, Sophia; Riese, Felix M.; Vorobyev, Artem; Mueller-Reif, Johannes B.; Geyer, Philipp E.; Strauss, Maximilian T.
In: Journal of Proteome Research, Vol. 22, No. 2, 2023, p. 359-367.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Transparent Exploration of Machine Learning for Biomarker Discovery from Proteomics and Omics Data
AU - Torun, Furkan M.
AU - Virreira Winter, Sebastian
AU - Doll, Sophia
AU - Riese, Felix M.
AU - Vorobyev, Artem
AU - Mueller-Reif, Johannes B.
AU - Geyer, Philipp E.
AU - Strauss, Maximilian T.
N1 - Publisher Copyright: © 2022 The Authors. Published by American Chemical Society.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - diagnostics
KW - machine learning
KW - mass spectrometry
KW - metabolome
KW - omics
KW - proteome
KW - transcriptome
U2 - 10.1021/acs.jproteome.2c00473
DO - 10.1021/acs.jproteome.2c00473
M3 - Journal article
C2 - 36426751
AN - SCOPUS:85143059596
VL - 22
SP - 359
EP - 367
JO - Journal of Proteome Research
JF - Journal of Proteome Research
SN - 1535-3893
IS - 2
ER -
ID: 329284339