CITATION.cff

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