Mining knowledge and data to discover intelligent molecular biomarkers: Prostate cancer i-Biomarkers

Created by W.Langdon from gp-bibliography.bib Revision:1.7662

@InProceedings{Floares:2010:SOFA,

• author = "Alexandru Floares and Ovidiu Balacescu and Carmen Floares and Loredana Balacescu and Tiberiu Popa and Oana Vermesan",
• title = "Mining knowledge and data to discover intelligent molecular biomarkers: Prostate cancer i-Biomarkers",
• booktitle = "4th International Workshop on Soft Computing Applications (SOFA 2010)",
• year = "2010",
• month = "15-17 " # jul,
• pages = "113--118",
• abstract = "Currently, there are some paradigm shifts in medicine, from the search for a single ideal biomarker, to the search for panels of molecules, and from a reductionistic to a systemic view, placing these molecules on functional networks. There is also a general trend to favour non-invasive biomarkers. Identifying non-invasive biomarkers in high-throughput data, having thousands of features and only tens of samples is not trivial. Here, we proposed a methodology and the related concepts to develop intelligent molecular biomarkers, via knowledge mining and knowledge discovery in data, illustrated on prostate cancer diagnosis. An informed feature selection is done by mining knowledge about pathways involved in prostate cancer, in specialised data bases. A knowledge discovery in data approach, with soft computing methods, is used to identify the relevant features and discover their relationships with clinical outcomes. The intelligent non-invasive diagnosis systems, is based on a team of mathematical models, discovered with genetic programming, and taking as inputs eight serum angiogenic molecules and PSA. This systems share with other intelligent systems we build, using this methodology but different soft computing techniques, and in different clinical settings - chronic hepatitis, bladder cancer, and prostate cancer - the best published accuracy, even 100percent. Soft computing could be a strong foundation for the newly emerging Knowledge-Based-Medicine. The impact on medical practice could be enormous. Instead of offering just hints to the clinicians, like Evidence-Based-Medicine, Knowledge-Based-Medicine which is made possible and co-exists with Evidence-Based-Medicine, offers intelligent clinical decision supports systems.",
• keywords = "genetic algorithms, genetic programming, PSA, bladder cancer, chronic hepatitis, data mining, evidence based medicine, intelligent clinical decision supports systems, intelligent molecular biomarkers, intelligent noninvasive diagnosis systems, knowledge based medicine, knowledge mining, prostate cancer i-biomarkers, serum angiogenic molecules, soft computing techniques, data mining, decision support systems, knowledge based systems, medical computing, patient diagnosis, uncertainty handling",
• DOI = "doi:10.1109/SOFA.2010.5565613",
• notes = "Discipulus Also known as \cite{5565613}",

}

Genetic Programming entries for Alexandru Floares Ovidiu Balacescu Carmen Floares Loredana Balacescu Tiberiu Popa Oana Vermesan

Citations