{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/pybiosig-optimizing-group-discrimination","title":"pyBioSig: optimizing group discrimination using genetic algorithms for biosignature discovery","arxiv_id":"1507.08911","date":"2015-07-30","proceeding":null,"authors":[],"abstract":"In medical sciences, a biomarker is \"a characteristic that is objectively\nmeasured and evaluated as an indicator of normal biological processes,\npathogenic processes, or pharmacologic responses to a therapeutic\nintervention\". Molecular experiments are providing rapid and systematic\napproaches to search for biomarkers, but because single-molecule biomarkers\nhave shown a disappointing lack of robustness for clinical diagnosis,\nresearchers have begun searching for distinctive sets of molecules, called\n\"biosignatures\". However, the most popular statistics are not appropriate for\ntheir identification, and the number of possible biosignatures to be tested is\nfrequently intractable. In the present work, we developed a \"multivariate\nfilter\" using genetic algorithms (GA) as a feature (gene) selector to optimize\na measure of intra-group cohesion and inter-group dispersion. This method was\nimplemented using Python and R (pyBioSig, available at\nhttps://github.com/fredgca/pybiosig under LGPL) and can be manipulated via\ngraphical interface or Python scripts. Using it, we were able to identify\nputative biosignatures composed by just a few genes and capable of recovering\nmultiple groups simultaneously in a hierarchical clustering, even ones that\nwere not recovered using the whole transcriptome, within a feasible length of\ntime using a personal computer. Our results allowed us to conclude that using\nGA to optimize our new intra-group cohesion and inter-group dispersion measure\nis a clear, effective, and computationally feasible strategy for the\nidentification of putative \"omical\" biosignatures that could support\ndiscrimination among multiple groups simultaneously.","url_abs":"http://arxiv.org/abs/1507.08911v1","url_pdf":"http://arxiv.org/pdf/1507.08911v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"pybiosig-optimizing-group-discrimination","repo_url":"https://github.com/fredgca/pybiosig","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}