Gene expression data analysis : a statistical and machine learning perspective
Author(s)
Bibliographic Information
Gene expression data analysis : a statistical and machine learning perspective
（A Chapman & Hall book）
Chapman & Hall/CRC, [2021]
- : hbk
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Note
Includes bibliographical references and index
Description and Table of Contents
Description
An introduction to the Central Dogma of molecular biology and information flow in biological systems.
A systematic overview of the methods for generating gene expression data.
Background knowledge on statistical modeling and machine learning techniques.
Detailed methodology of analyzing gene expression data with an example case study.
Clustering methods for finding co-expression patterns from microarray, bulkRNA and scRNA data.
A large number of practical tools, systems and repositories that are useful for computational biologists to create, analyze and validate biologically relevant gene expression patterns.
Suitable for multi-disciplinary researchers and practitioners in computer science and biological sciences.
Table of Contents
Preface. Acknowledgements. Abstract. Authors. Introduction. Information Flow in Biological Systems. Gene Expression Data Generation. Statistical Foundations and Machine Learning. Co-expression Analysis. Differential Expression Analysis. Tools and Systems. Concluding Remarks and Research Challenges. Index. Glossary.
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