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  "Authors@R": "person(\"Sokratis\", \"Kariotis\", role = c(\"aut\", \"cre\"),\nemail = \"sokratiskariotis@gmail.com\",\ncomment = c(ORCID = \"0000-0001-9993-6017\"))",
  "Description": "Symptomatic heterogeneity in complex diseases reveals\ndifferences in molecular states that need to be investigated.\nHowever, selecting the numerous parameters of an exploratory\nclustering analysis in RNA profiling studies requires deep\nunderstanding of machine learning and extensive computational\nexperimentation. Tools that assist with such decisions without\nprior field knowledge are nonexistent and further gene\nassociation analyses need to be performed independently. We\nhave developed a suite of tools to automate these processes and\nmake robust unsupervised clustering of transcriptomic data more\naccessible through automated machine learning based functions.\nThe efficiency of each tool was tested with four datasets\ncharacterised by different expression signal strengths. Our\ntoolkit’s decisions reflected the real number of stable\npartitions in datasets where the subgroups are discernible.\nEven in datasets with less clear biological distinctions,\nstable subgroups with different expression profiles and\nclinical associations were found.",
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