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Overview of Commonly Used Bioinformatics Methods and Their Applications
IZET M. KAPETANOVICa,
SIMON ROSENFELDb AND
GRANT IZMIRLIANb
aChemopreventive Agent Development Research Group and bBiometry Research Group, Division of Cancer Prevention, National Cancer Institute, Bethesda, Maryland, USA
Address for correspondence: Izet M. Kapetanovic, Chemopreventive Agent Development Research Group, Division of Cancer Prevention, National Cancer Institute, Bethesda, MD 20892-7322. Voice: 301-435-5011; fax: 301-402-0553. kapetani{at}mail.nih.gov
Ann. N.Y. Acad. Sci. 1020: 10-21 (2004).
Bioinformatics, in its broad sense, involves application of computer processes to solve biological problems. A wide range of computational tools are needed to effectively and efficiently process large amounts of data being generated as a result of recent technological innovations in biology and medicine. A number of computational tools have been developed or adapted to deal with the experimental riches of complex and multivariate data and transition from data collection to information or knowledge. These include a wide variety of clustering and classification algorithms, including self-organized maps (SOM), artificial neural networks (ANN), support vector machines (SVM), fuzzy logic, and even hyphenated techniques as neuro-fuzzy networks. These bioinformatics tools are being evaluated and applied in various medical areas including early detection, risk assessment, classification, and prognosis of cancer. The goal of these efforts is to develop and identify bioinformatics methods with optimal sensitivity, specificity, and predictive capabilities.
Key Words: bioinformatics • data mining • cancer • early detection • risk assessment • hierarchical clustering • neural networks • support vector machines • fuzzy logic • genomics • proteomics • drug discovery
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