Mining knowledge and data to discover intelligent molecular biomarkers: Prostate cancer i-Biomarkers
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Floares:2010:SOFA,
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author = "Alexandru Floares and Ovidiu Balacescu and
Carmen Floares and Loredana Balacescu and Tiberiu Popa and
Oana Vermesan",
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title = "Mining knowledge and data to discover intelligent
molecular biomarkers: Prostate cancer i-Biomarkers",
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booktitle = "4th International Workshop on Soft Computing
Applications (SOFA 2010)",
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year = "2010",
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month = "15-17 " # jul,
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pages = "113--118",
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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.",
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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",
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DOI = "doi:10.1109/SOFA.2010.5565613",
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notes = "Discipulus Also known as \cite{5565613}",
- }
Genetic Programming entries for
Alexandru Floares
Ovidiu Balacescu
Carmen Floares
Loredana Balacescu
Tiberiu Popa
Oana Vermesan
Citations