Data Mining for Genomics and Proteomics: Analysis of Gene and Protein Expression Data

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9780470163733: Data Mining for Genomics and Proteomics: Analysis of Gene and Protein Expression Data
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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.

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Practical methods for mining gene and protein expressiondata

Proper analysis and mining of the rapidly growing amount ofavailable genomic and proteomic data is vital for advances inbiomedical research. Data Mining for Genomics and Proteomicsdescribes efficient methods for analysis of gene and proteinexpression data. Dr. Darius Dziuda demonstrates step by step howbiomedical studies can and should be performed to maximize thechance of extracting new and useful biomedical knowledge fromavailable data. Readers receive clear guidance on when to useparticular data mining methods and why, along with the reasons whysome popular approaches can lead to inferior results.

This book covers all aspects of gene and protein expressionanalysis—from technology, data preprocessing, qualityassessment, and basic exploratory analysis to unsupervised andsupervised learning algorithms, feature selection, and biomarkerdiscovery. Also presented is a novel method for identification ofthe Informative Set of Genes, defined as a set containingall information significant for the differentiation of classesrepresented in training data. Special attention is given tomultivariate biomarker discovery leading to parsimonious andgeneralizable classifiers. In addition, exercises and examples ofhands-on analysis of real-world gene expression data sets givereaders an opportunity to put the methods they have learned topractical use.

Data Mining for Genomics and Proteomics is an excellentresource for data mining specialists, bioinformaticians,computational biologists, biomedical scientists, computerscientists, molecular biologists, and life scientists. It is alsoideal for upper-level undergraduate and graduate-level students ofbioinformatics, data mining, computational biology, and biomedicalsciences, as well as anyone interested in efficient methods ofknowledge discovery based on high-dimensional data.

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.

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