| Title: |
Biological Data Mining |
| Author(s): |
G. Tzanis, C. Berberidis, I. Vlahavas.
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| Availability: |
Click here to download the PDF (Acrobat Reader) file (7 pages).
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| Keywords: |
data mining, bioinformatics, molecular biology, genomics, proteomics, gene expression analysis, microarray, classification, clustering, gene selection.
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| Appeared in: |
Encyclopedia of Database Technologies and Applications, Laura C. Rivero, Jorge H. Doorn and Viviana E. Ferraggine (Eds.), Idea Group Publishing, pp. 35-41, 2005.
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| Abstract: |
[... ]Recently, the collection of biological data has been increasing at explosive rates due to improvements of existing technologies and the introduction of new ones such as the microarrays. These technological advances have assisted the conduct of large scale experiments and research programs. An important example is the Human Genome Project, that was founded in 1990 by the U.S. Department of Energy and the U.S. National Institutes of Health (NIH) and was completed in 2003 (U.S. Department of Energy Office of Science, 2004). A representative example of the rapid biological data accumulation is the exponential growth of GenBank (Figure 1), the U.S. NIH genetic sequence database. (National Center for Biotechnology Information, 2004). The explosive growth in the amount of biological data demands the use of computers for the organization, the maintenance and the analysis of these data. |
| See also : |
The official web page
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This paper has been cited by the following:
| 1 |
Ahmed Khorsi, "Knowledge Representation, A.I applications and Data-mining", Conférence Internationale sur l’Informatique et ses Applications, Algeria, 2006 |
| 2 |
K. Kianmehr, M. Alshalalfa, R. Alhajj. Fuzzy clustering-based discretization for gene expression classification, Knowledge and Information Systems, 2009. |
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