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Hierarchical Profiling, Scoring, and Applications in Bioinformatics

Hierarchical Profiling, Scoring, and Applications in Bioinformatics

Li Liao
Copyright: © 2008 |Pages: 15
ISBN13: 9781599045283|ISBN10: 1599045281|ISBN13 Softcover: 9781616926564|EISBN13: 9781599045306
DOI: 10.4018/978-1-59904-528-3.ch008
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MLA

Liao, Li. "Hierarchical Profiling, Scoring, and Applications in Bioinformatics." Mathematical Methods for Knowledge Discovery and Data Mining, edited by Giovanni Felici and Carlo Vercellis, IGI Global, 2008, pp. 131-145. https://doi.org/10.4018/978-1-59904-528-3.ch008

APA

Liao, L. (2008). Hierarchical Profiling, Scoring, and Applications in Bioinformatics. In G. Felici & C. Vercellis (Eds.), Mathematical Methods for Knowledge Discovery and Data Mining (pp. 131-145). IGI Global. https://doi.org/10.4018/978-1-59904-528-3.ch008

Chicago

Liao, Li. "Hierarchical Profiling, Scoring, and Applications in Bioinformatics." In Mathematical Methods for Knowledge Discovery and Data Mining, edited by Giovanni Felici and Carlo Vercellis, 131-145. Hershey, PA: IGI Global, 2008. https://doi.org/10.4018/978-1-59904-528-3.ch008

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Abstract

Recently, clustering and classification methods have seen many applications in bioinformatics. Some are simply straightforward applications of existing techniques, but most have been adapted to cope with peculiar features of the biological data. Many biological data take a form of vectors, whose components correspond to attributes characterizing the biological entities being studied. Comparing these vectors, a.k.a. profiles, are a crucial step for most clustering and classification methods. We review the recent developments related to hierarchical profiling where the attributes are not independent, but rather are correlated in a hierarchy. Hierarchical profiling arises in a wide range of bioinformatics problems, including protein homology detection, protein family classification, and metabolic pathway clustering. We discuss in detail several clustering and classification methods where hierarchical correlations are tackled with in effective and efficient ways, by incorporation of domain specific knowledge. Relations to other statistical learning methods and more potential applications are also discussed.

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