Data Mining for Bioinformatics
1st Edition
Published on 6. November 2012
Book
Hardback
348 pages
978-0-8493-2801-5 (ISBN)
Description
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:
Supplies a complete overview of the evolution of the field and its intersection with computational learning
Describes the role of data mining in analyzing large biological databases-explaining the breath of the various feature selection and feature extraction techniques that data mining has to offer
Focuses on concepts of unsupervised learning using clustering techniques and its application to large biological data
Covers supervised learning using classification techniques most commonly used in bioinformatics-addressing the need for validation and benchmarking of inferences derived using either clustering or classification
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
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:
Supplies a complete overview of the evolution of the field and its intersection with computational learning
Describes the role of data mining in analyzing large biological databases-explaining the breath of the various feature selection and feature extraction techniques that data mining has to offer
Focuses on concepts of unsupervised learning using clustering techniques and its application to large biological data
Covers supervised learning using classification techniques most commonly used in bioinformatics-addressing the need for validation and benchmarking of inferences derived using either clustering or classification
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
More details
Language
English
Place of publication
Bosa Roca
United States
Publishing group
Taylor & Francis Inc
Target group
Professional and scholarly
bioinformatics software engineers/developers; bioinformatician; bioinformatics scientists and support specialists; data mining analysts/architects; data mining engineers and support specialists; faculty; post doctoral researchers
Product notice
Paper over boards
Illustrations
92 s/w Abbildungen, 15 s/w Tabellen
Dimensions
Height: 240 mm
Width: 161 mm
Thickness: 23 mm
Weight
690 gr
ISBN-13
978-0-8493-2801-5 (9780849328015)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Sumeet Dua | Pradeep Chowriappa
Data Mining for Bioinformatics
Book
09/2019
1st Edition
CRC Press
€94.40
Shipment within 15-20 days
Sumeet Dua | Pradeep Chowriappa
Data Mining for Bioinformatics
E-Book
11/2012
1st Edition
CRC Press
€69.99
Available for download
Sumeet Dua | Pradeep Chowriappa
Data Mining for Bioinformatics
E-Book
11/2012
1st Edition
CRC Press
€69.99
Available for download
Persons
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
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
Content
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.