Description
Biological Data Mining
Chapman & Hall/CRC Data Mining and Knowledge Discovery Series
Coordinators: Chen Jake Y., Lonardi Stefano
Language: EnglishSubjects for Biological Data Mining:
Keywords
C6847 Chapter; PPI Network; bioinformatics; SVM; Jake Chen; Data Set; data mining; Microarray Data; biology; Degree Distribution; Reference Genome; Scop Database; Structure Prediction; BNs; MSAs; Geometric Hashing; Microarray Gene Expression Data; RNA Secondary Structure; MS Spectrum; UPR; CNS Rat; Energy Density; Secondary Structure; Biomedical Text Mining; Tag SNPs; DNA Sequence; Peptide Retention Time; Frequent Subgraphs; Da Ta
Publication date: 06-2017
· 15.6x23.4 cm · Paperback
Publication date: 09-2009
656 p. · 15.6x23.4 cm · Hardback
Description
/li>Contents
/li>Readership
/li>Biography
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Like a data-guzzling turbo engine, advanced data mining has been powering post-genome biological studies for two decades. Reflecting this growth, Biological Data Mining presents comprehensive data mining concepts, theories, and applications in current biological and medical research. Each chapter is written by a distinguished team of interdisciplinary data mining researchers who cover state-of-the-art biological topics.
The first section of the book discusses challenges and opportunities in analyzing and mining biological sequences and structures to gain insight into molecular functions. The second section addresses emerging computational challenges in interpreting high-throughput Omics data. The book then describes the relationships between data mining and related areas of computing, including knowledge representation, information retrieval, and data integration for structured and unstructured biological data. The last part explores emerging data mining opportunities for biomedical applications.
This volume examines the concepts, problems, progress, and trends in developing and applying new data mining techniques to the rapidly growing field of genome biology. By studying the concepts and case studies presented, readers will gain significant insight and develop practical solutions for similar biological data mining projects in the future.
Sequence, Structure, and Function. Genomics, Transcriptomics, and Proteomics. Functional and Molecular Interaction Networks. Literature, Ontology, and Knowledge Integration. Genome Medicine Applications.
Jake Y. Chen is an assistant professor of informatics at Indiana University, an assistant professor of computer science at Purdue University, and director of the Indiana Center for Systems Biology and Personalized Medicine.
Stefano Lonardi is an associate professor of computer science and engineering at the University of California, Riverside.
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