Data Mining for Genomics and Proteomics: Analysis of Gene and Protein Expression Data - ISBN:0470163739

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

Date: 21 April 2011, 00:30

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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.

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