Development of high throughput technologies in molecular biology during the last two decades has contributed to the production of tremendous amounts of data. Microarray and RNA-sequencing are two such widely used high throughput technologies for monitoring the expression patterns of thousands of genes simultaneously. Data produced from such experiments are voluminous (both in dimensionality and numbers of instances) and evolving in nature. Analysis of huge amounts of data towards the identification of interesting patterns that are relevant for a given biological question requires high performance computational infrastructure as well as efficient machine learning algorithms. Cross-communication of ideas between biologists and computer scientists remains a big challenge.Gene Expression Data Analysis: A Statistical and Machine Learning Perspective has been written keeping a multi-disciplinary audience in mind. The book discusses gene expression data analysis from molecular biology, machine learning and statistical perspectives. Readers will be able to acquire both theoretical as well as practical knowledge of methods for identification of novel patterns of high biological significance. To measure the effectiveness of such algorithms, we discuss statistical and biological performance metrics that can be used in real life or in a simulated environment. This book discusses a large number of benchmark algorithms, tools, systems and repositories that are commonly used in analyzing gene expression data and validating results.This book will benefit students, researchers and practitioners in biology, medicine, and computer science by enabling them to acquire in-depth knowledge in statistical and machine learning based methods for analyzing gene expression data.Key features:

An introduction to the Central Dogma of molecular biology and information flow in biological systems.

A systematic

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