Data Mining for Bioinformatics enables researchers to meet the challenge of mining vast amounts of biomolecular data to discover real knowledge. Covering theory, algorithms, and methodologies, as well as data mining technologies, it presents a thorough discussion of data-intensive computations used in data mining applied to bioinformatics. The book explains data mining design concepts to build applications and systems. Showing how to prepare raw data for the mining process, the text is filled with heuristics that speed the data mining process.
Data Mining for Bioinformatics enables researchers to meet the challenge of mining vast amounts of biomolecular data to discover real knowledge. Covering theory, algorithms, and methodologies, as well as data mining technologies, it presents a thorough discussion of data-intensive computations used in data mining applied to bioinformatics. The book explains data mining design concepts to build applications and systems. Showing how to prepare raw data for the mining process, the text is filled with heuristics that speed the data mining process.
Sumeet Dua is an Upchurch endowed professor of computer science and interim director of computer science, electrical engineering, and electrical engineering technology in the College of Engineering and Science at Louisiana Tech University. He obtained his PhD in computer science from Louisiana State University in 2002. He has coauthored/edited 3 books, has published over 50 research papers in leading journals and conferences, and has advised over 22 graduate thesis and dissertations in the areas of data mining, knowledge discovery, and computational learning in high-dimensional datasets. NIH, NSF, AFRL, AFOSR, NASA, and LA-BOR have supported his research. He frequently serves as a panelist for the NSF and NIH (over 17 panels) and has presented over 25 keynotes, invited talks, and workshops at international conferences and educational institutions. He has also served as the overall program chair for three international conferences and as a chair for multiple conference tracks in the areas of data mining applications and information intelligence. He is a senior member of the IEEE and the ACM. His research interests include information discovery in heterogeneous and distributed datasets, semisupervised learning, content-based feature extraction and modeling, and pattern tracking. Pradeep Chowriappa is a research assistant professor in the College of Engineering and Science at Louisiana Tech University. His research focuses on the application of data mining algorithms and frameworks on biological and clinical data. Before obtaining his PhD in computer analysis and modeling from Louisiana Tech University in 2008, he pursued a yearlong internship at the Indian Space Research Organization (ISRO), Bangalore, India. He received his masters in computer applications from the University of Madras, Chennai, India, in 2003 and his bachelor's in science and engineering from Loyola Academy, Secunderabad, India, in 2000. His research interests
Inhaltsangabe
Introduction. Bioinformatics Data for Knowledge Discovery: Sources and Organization. Data Preparation for Bioinformatics. Classification and Clustering. Protein Data Mining. Gene Expression Data Mining. Medical Image Mining.
Introduction. Bioinformatics Data for Knowledge Discovery: Sources and Organization. Data Preparation for Bioinformatics. Classification and Clustering. Protein Data Mining. Gene Expression Data Mining. Medical Image Mining.
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