This book focuses on the development and application of the latest advanced data mining, machine learning, and visualization techniques for the identification of interesting, significant, and novel patterns in gene expression microarray data. Biomedical researchers will find this book invaluable for learning the cutting-edge methods for analyzing gene expression microarray data. Specifically, the coverage includes the following state-of-the-art methods: gene-based analysis - the latest novel clustering algorithms to identify co-expressed genes and coherent patterns in gene expression microarray data sets; sample-based analysis - supervised and unsupervised methods for the reduction of the gene dimensionality to select significant genes. A series of approaches to disease classification and discovery are also described; pattern-based analysis - methods for ascertaining the relationship between (subsets of) genes and (subsets of) samples. Various novel pattern-based clustering algorithms to find the coherent patterns embedded in the sub-attribute spaces are discussed; and visualization tools - various methods for gene expression data visualization. The visualization process is intended to transform the gene expression data set from high-dimensional space into a more easily understood two- or three-dimensional space.
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1 of 1 people found the following review helpful:
5.0 out of 5 stars
A great book for statistician at all level,
By Yiyi (Portland, OR USA) - See all my reviews
This review is from: Advanced Analysis of Gene Expression Microarray Data (Science, Engineering, and Biology Informatics) (Hardcover)
This book is very well written, in great detail and clarity. The title "Advanced" sounds daunting to some intermediate or entry level statisticians, but the math details in the book is very understandable, presented as necessary support in order to understand the explained statistical methods.I would highly recommend this book to statisticians with all statistical backgrounds who are new to the area of microarray data analysis. This book is perfect as a textbook for a first course of microarray data analysis too.
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Inside This Book
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Key Phrases - Statistically Improbable Phrases (SIPs):
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phenotype quality, coherent gene clusters, coherent sample sets, informative genes, gene pies, virtual genes, set enumeration tree, affinity search technique, phenotype structure, colon cancer data, gene expression data sets, microarray data sets, gene expression microarray data, plaid model, harmonic projection, reference partition, subspace clusters, attraction tree, genomic data sets, downward closure property, leukemia data, intermediate genes, gene expression matrix, mining frequent itemsets, knn method Key Phrases - Capitalized Phrases (CAPs): (learn more)
Sinaucr Associates, Sample-Gene Search, Genes Fig, Daxin Jiang, Two N-dimensional, Time Point Time Point Time Point, Time Points Pearson New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
phenotype quality, coherent gene clusters, coherent sample sets, informative genes, gene pies, virtual genes, set enumeration tree, affinity search technique, phenotype structure, colon cancer data, gene expression data sets, microarray data sets, gene expression microarray data, plaid model, harmonic projection, reference partition, subspace clusters, attraction tree, genomic data sets, downward closure property, leukemia data, intermediate genes, gene expression matrix, mining frequent itemsets, knn method Key Phrases - Capitalized Phrases (CAPs): (learn more)
Sinaucr Associates, Sample-Gene Search, Genes Fig, Daxin Jiang, Two N-dimensional, Time Point Time Point Time Point, Time Points Pearson New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 43
books:
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- in Back Matter (1), Back Matter (2), and Back Matter (3)
- Molecular Biology by Robert F. Weaver on page 13, and Back Matter
- Life: The Science of Biology, 7th Edition by William K. Purves on page 13, and Back Matter
- Analyzing Microarray Gene Expression Data (Wiley Series in Probability and Statistics) by Geoffrey J. McLachlan in Back Matter
- Cell and Molecular Biology: Concepts and Experiments by Gerald Karp in Back Matter
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