Welcome to the InfoSci Platform
Hierarchical Profiling, Scoring and Applications in Bioinformatics

Hierarchical Profiling, Scoring and Applications in Bioinformatics

Li Liao
Copyright: © 2006 |Pages: 19
ISBN13: 9781591408635|ISBN10: 1591408636|ISBN13 Softcover: 9781591408642|EISBN13: 9781591408659
DOI: 10.4018/978-1-59140-863-5.ch002
Cite Chapter Cite Chapter

MLA

Liao, Li. "Hierarchical Profiling, Scoring and Applications in Bioinformatics." Advanced Data Mining Technologies in Bioinformatics, edited by Hui-Huang Hsu, IGI Global, 2006, pp. 13-31. https://doi.org/10.4018/978-1-59140-863-5.ch002

APA

Liao, L. (2006). Hierarchical Profiling, Scoring and Applications in Bioinformatics. In H. Hsu (Ed.), Advanced Data Mining Technologies in Bioinformatics (pp. 13-31). IGI Global. https://doi.org/10.4018/978-1-59140-863-5.ch002

Chicago

Liao, Li. "Hierarchical Profiling, Scoring and Applications in Bioinformatics." In Advanced Data Mining Technologies in Bioinformatics, edited by Hui-Huang Hsu, 13-31. Hershey, PA: IGI Global, 2006. https://doi.org/10.4018/978-1-59140-863-5.ch002

Export Reference

Mendeley
Favorite

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, aka 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 in effective and efficient ways, by incorporation of domain-specific knowledge. Relations to other statistical learning methods and more potential applications are also discussed.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.