New PDF release: Data Mining and Bioinformatics: First International

By Simon Mercer (auth.), Mehmet M. Dalkilic, Sun Kim, Jiong Yang (eds.)

ISBN-10: 3540689702

ISBN-13: 9783540689706

This quantity comprises the papers awarded on the inaugural workshop on facts Mining and Bioinformatics on the thirty second foreign convention on Very huge info Bases (VLDB). the aim of this workshop was once to start bringing - gether researchersfrom database, information mining, and bioinformatics components to assist leverage respective successes in each one to the others. We additionally desire to reveal the richness, complexity, and demanding situations during this sector that comprises mining very huge advanced organic information that may merely develop in dimension and complexity as geno- scale high-throughput concepts turn into extra regimen. the issues are s- ?ciently di?erent adequate from conventional facts mining difficulties (outside of lifestyles sciences) that novel techniques has to be taken to info mine during this region. The workshop used to be held in Seoul, Korea, on September eleven, 2006. We bought 30 submissions in accordance with the decision for papers. every one subm- sion was once assigned to at the least 3 individuals of this system Committee. this system Committee mentioned the submission electronically, judging them on their significance, originality, readability, relevance, and appropriateness to the - pected viewers. this system Committee chosen 15 papers for presentation. those papers arein the areasof microarraydata research, bioinformaticssystem and textual content retrieval, program of gene expression information, and series research. end result of the structure of the workshop and the excessive variety of submissions, many solid papers couldn't be included.

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Additional info for Data Mining and Bioinformatics: First International Workshop, VDMB 2006, Seoul, Korea, September 11, 2006, Revised Selected Papers

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2. The Gene Ontology Consortium. Creating the gene ontology resource: design and implementation. Genome Research, 11(8):1425-1433, 2001. 3. J. J. A. Winzeler,L. Steinmetz, A. Conway, L. G. E. Gabrielian, D. J. W. Davis. A genome-wide transcriptional analysis of the mitotic cell cycle. Molecular Cell, 2:5-73, 1998. 4. S. Chung, J. Jun, and D. McLeod. Mining gene expression datasets using densitybased clustering. In Proceedings of ACM CIKM International Conference on Information and Knowledge Management, 2004.

4. S. Chung, J. Jun, and D. McLeod. Mining gene expression datasets using densitybased clustering. In Proceedings of ACM CIKM International Conference on Information and Knowledge Management, 2004. 5. O. E. G. Stork. ). Wiley, New York, 2001. 6. A. Gasch, and M. Eisen. Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering. Genome Biology, 3(11):1-22, 2002. 7. R. Golub et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.

While these methods on their own are not novel, we combine them to improve the statistical reliability of decision trees for microarray data. This paper is organized as follows. 5. Section 3 gives a list of related work in decision trees and microarray classification. Our method is detailed in Section 4, followed by experimental results with both artificial and real data in Section 5. Finally, Section 6 closes this paper by summarizing our method. 2 Background A data set D consists of m examples with n attributes (or features) each.

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Data Mining and Bioinformatics: First International Workshop, VDMB 2006, Seoul, Korea, September 11, 2006, Revised Selected Papers by Simon Mercer (auth.), Mehmet M. Dalkilic, Sun Kim, Jiong Yang (eds.)


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