Hardback : £66.71
Covering theory, algorithms, and methodologies, as well as data mining technologies, Data Mining for Bioinformatics provides a comprehensive discussion of data-intensive computations used in data mining with applications in bioinformatics. It supplies a broad, yet in-depth, overview of the application domains of data mining for bioinformatics to help readers from both biology and computer science backgrounds gain an enhanced understanding of this cross-disciplinary field.
The book offers authoritative coverage of data mining techniques, technologies, and frameworks used for storing, analyzing, and extracting knowledge from large databases in the bioinformatics domains, including genomics and proteomics. It begins by describing the evolution of bioinformatics and highlighting the challenges that can be addressed using data mining techniques. Introducing the various data mining techniques that can be employed in biological databases, the text is organized into four sections:
The book describes the various biological databases prominently referred to in bioinformatics and includes a detailed list of the applications of advanced clustering algorithms used in bioinformatics. Highlighting the challenges encountered during the application of classification on biologica
Show moreCovering theory, algorithms, and methodologies, as well as data mining technologies, Data Mining for Bioinformatics provides a comprehensive discussion of data-intensive computations used in data mining with applications in bioinformatics. It supplies a broad, yet in-depth, overview of the application domains of data mining for bioinformatics to help readers from both biology and computer science backgrounds gain an enhanced understanding of this cross-disciplinary field.
The book offers authoritative coverage of data mining techniques, technologies, and frameworks used for storing, analyzing, and extracting knowledge from large databases in the bioinformatics domains, including genomics and proteomics. It begins by describing the evolution of bioinformatics and highlighting the challenges that can be addressed using data mining techniques. Introducing the various data mining techniques that can be employed in biological databases, the text is organized into four sections:
The book describes the various biological databases prominently referred to in bioinformatics and includes a detailed list of the applications of advanced clustering algorithms used in bioinformatics. Highlighting the challenges encountered during the application of classification on biologica
Show moreIntroduction. 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.
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
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