Data Mining for Genomics and Proteomics: Analysis of Gene and Protein Expression Data (Wiley Series on Methods and Applications in Data Mining) - Hardcover

Dziuda, Darius M.

Hardcover

ISBN 10: 0470163739 ISBN 13: 9780470163733

Publisher: Wiley-Interscience, 2010

Synopsis

Data Mining for Genomics and Proteomics uses pragmatic examples and a complete case study to demonstrate step-by-step how biomedical studies can be used to maximize the chance of extracting new and useful biomedical knowledge from data. It is an excellent resource for students and professionals involved with gene or protein expression data in a variety of settings.

About this title

About the Author

Darius M. Dziuda, PhD, is Associate Professor of Data Mining and Statistics in the Department of Mathematical Sciences at Central Connecticut State University (CCSU). His research and professional activities have been focused on efficient data mining of biomedical data and on methods for identification of parsimonious multivariate biomarkers for medical diagnosis, prognosis, personalized medicine, and drug discovery. For CCSU's data mining program, Dr. Dziuda developed and teaches graduate-level courses on Data Mining for Genomics and Proteomics and on Biomarker Discovery.

From the Back Cover

Practical methods for mining gene and protein expression data

Proper analysis and mining of the rapidly growing amount of available genomic and proteomic data is vital for advances in biomedical research. Data Mining for Genomics and Proteomics describes efficient methods for analysis of gene and protein expression data. Dr. Darius Dziuda demonstrates step by step how biomedical studies can and should be performed to maximize the chance of extracting new and useful biomedical knowledge from available data. Readers receive clear guidance on when to use particular data mining methods and why, along with the reasons why some popular approaches can lead to inferior results.

This book covers all aspects of gene and protein expression analysis--from technology, data preprocessing, quality assessment, and basic exploratory analysis to unsupervised and supervised learning algorithms, feature selection, and biomarker discovery. Also presented is a novel method for identification of the Informative Set of Genes, defined as a set containing all information significant for the differentiation of classes represented in training data. Special attention is given to multivariate biomarker discovery leading to parsimonious and generalizable classifiers. In addition, exercises and examples of hands-on analysis of real-world gene expression data sets give readers an opportunity to put the methods they have learned to practical use.

Data Mining for Genomics and Proteomics is an excellent resource for data mining specialists, bioinformaticians, computational biologists, biomedical scientists, computer scientists, molecular biologists, and life scientists. It is also ideal for upper-level undergraduate and graduate-level students of bioinformatics, data mining, computational biology, and biomedical sciences, as well as anyone interested in efficient methods of knowledge discovery based on high-dimensional data.

Proper analysis and mining of the rapidly growing amount of available genomic and proteomic data is vital for advances in biomedical research. Data Mining for Genomics and Proteomics describes efficient methods for analysis of gene and protein expression data. Dr. Darius Dziuda demonstrates step by step how biomedical studies can and should be performed to maximize the chance of extracting new and useful biomedical knowledge from available data. Readers receive clear guidance on when to use particular data mining methods and why, along with the reasons why some popular approaches can lead to inferior results.

This book covers all aspects of gene and protein expression analysis—from technology, data preprocessing, quality assessment, and basic exploratory analysis to unsupervised and supervised learning algorithms, feature selection, and biomarker discovery. Also presented is a novel method for identification of the Informative Set of Genes, defined as a set containing all information significant for the differentiation of classes represented in training data. Special attention is given to multivariate biomarker discovery leading to parsimonious and generalizable classifiers. In addition, exercises and examples of hands-on analysis of real-world gene expression data sets give readers an opportunity to put the methods they have learned to practical use.

Data Mining for Genomics and Proteomics is an excellent resource for data mining specialists, bioinformaticians, computational biologists, biomedical scientists, computer scientists, molecular biologists, and life scientists. It is also ideal for upper-level undergraduate and graduate-level students of bioinformatics, data mining, computational biology, and biomedical sciences, as well as anyone interested in efficient methods of knowledge discovery based on high-dimensional data.

Edition details

• Publisher: Wiley-Interscience
• Publication date: 2010
• Language: English
• ISBN 10: 0470163739
• ISBN 13: 9780470163733
• Binding: Hardcover
• Edition number: 1
• Number of pages: 336