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# CITATION file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "omada" in publications use:'
type: software
license: GPL-3.0-only
title: 'omada: Machine learning tools for automated transcriptome clustering analysis'
version: 1.7.0
doi: 10.32614/CRAN.package.omada
abstract: Symptomatic heterogeneity in complex diseases reveals differences in molecular
  states that need to be investigated. However, selecting the numerous parameters
  of an exploratory clustering analysis in RNA profiling studies requires deep understanding
  of machine learning and extensive computational experimentation. Tools that assist
  with such decisions without prior field knowledge are nonexistent and further gene
  association analyses need to be performed independently. We have developed a suite
  of tools to automate these processes and make robust unsupervised clustering of
  transcriptomic data more accessible through automated machine learning based functions.
  The efficiency of each tool was tested with four datasets characterised by different
  expression signal strengths. Our toolkit’s decisions reflected the real number of
  stable partitions in datasets where the subgroups are discernible. Even in datasets
  with less clear biological distinctions, stable subgroups with different expression
  profiles and clinical associations were found.
authors:
- family-names: Kariotis
  given-names: Sokratis
  email: sokratiskariotis@gmail.com
  orcid: https://orcid.org/0000-0001-9993-6017
repository: https://bioc.r-universe.dev
commit: 3d3c329917ecbb123149635038310d75e98bbabf
contact:
- family-names: Kariotis
  given-names: Sokratis
  email: sokratiskariotis@gmail.com
  orcid: https://orcid.org/0000-0001-9993-6017