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

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@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