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CITATION.cff
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CITATION.cff
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cff-version: 1.2.0
message: If you use OmicLearn, please cite the article from preferred-citation.
title: OmicLearn
type: software
license: Apache-2.0
repository-code: 'https://github.com/MannLabs/OmicLearn/'
url: 'http://OmicLearn.org'
version: 1.4
preferred-citation:
title: "Transparent Exploration of Machine Learning for Biomarker Discovery from Proteomics and Omics Data"
type: article
authors:
- given-names: Furkan M.
family-names: Torun
email: furkanmtorun@gmail.com
- given-names: Sebastian
family-names: Virreira Winter
- given-names: Sophia
family-names: Doll
- given-names: Felix M.
family-names: Riese
- given-names: Artem
family-names: Vorobyev
- given-names: Johannes B.
family-names: Mueller-Reif
- given-names: Philipp E.
family-names: Geyer
- given-names: Maximilian T.
family-names: Strauss
email: maximilian.strauss@cpr.ku.dk
year: 2022
doi: "10.1021/acs.jproteome.2c00473"
journal: "Journal of Proteome Research"
url: https://pubs.acs.org/doi/10.1021/acs.jproteome.2c00473
abstract: >-
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.
keywords:
- machine learning
- data science
- mass spectrometry
- diagnostics
- omics
- proteome
- metabolome
- transcriptome