Gene Expression Data Analysis: A Statistical and Machine Learning Perspective

The development of high-throughput technologies in molecular biology has led to the production of vast amounts of data. This book discusses gene expression data analysis from molecular biology, machine learning, and statistical perspectives, providing theoretical and practical knowledge of methods for identifying novel patterns of high biological significance. It also discusses benchmark algorithms, tools, systems, and repositories commonly used in analyzing gene expression data and validating results. Suitable for multidisciplinary researchers and practitioners in computer science and biological sciences.

Format: Hardback
Length: 360 pages
Publication date: 22 November 2021
Publisher: Taylor & Francis Ltd

The development of high-throughput technologies in molecular biology has led to the generation of vast amounts of data, particularly through microarray and RNA sequencing. These technologies enable simultaneous monitoring of the expression patterns of thousands of genes, generating data that is both voluminous and evolving in nature.

Analyzing such large datasets requires high-performance computational infrastructure and efficient machine learning algorithms to identify interesting patterns that are relevant to a given biological question. However, cross-communication between biologists and computer scientists remains a significant challenge.

Gene Expression Data Analysis: A Statistical and Machine Learning Perspective aims to address this gap by providing a comprehensive and interdisciplinary approach to gene expression data analysis. The book discusses gene expression data analysis from molecular biology, machine learning, and statistical perspectives, providing readers with both theoretical and practical knowledge of methods for identifying novel patterns of high biological significance.

To measure the effectiveness of these algorithms, the book discusses statistical and biological performance metrics that can be used in real-life or simulated environments. It also provides a comprehensive list of benchmark algorithms, tools, systems, and repositories commonly used in analyzing gene expression data and validating results.

The book is designed for a multidisciplinary audience, including students, researchers, and practitioners in biology, medicine, and computer science. It will enable them to acquire in-depth knowledge in statistical and machine-learning-based methods for analyzing gene expression data, which is crucial in understanding biological processes and developing new treatments.

Some key features of the book include:

An interdisciplinary approach: The book brings together experts from different fields, including molecular biology, statistics, and machine learning, to provide a comprehensive perspective on gene expression data analysis.

Comprehensive coverage: The book covers a wide range of topics, including data preprocessing, feature selection, classification, and regression, using both statistical and machine learning methods.

Real-world examples: The book includes case studies and real-world datasets to demonstrate the practical application of the discussed methods and algorithms.

Benchmark algorithms: The book provides a comprehensive list of benchmark algorithms, tools, systems, and repositories that are commonly used in analyzing gene expression data.

Accessible language: The book is written in an accessible and engaging style, making it suitable for students and practitioners with a limited background in statistics or machine learning.

Overall, Gene Expression Data Analysis: A Statistical and Machine Learning Perspective is a valuable resource for anyone interested in analyzing gene expression data and gaining insights into biological processes. It will serve as a valuable reference for students, researchers, and practitioners in biology, medicine, and computer science.

Weight: 857g
Dimension: 234 x 156 (mm)
ISBN-13: 9780367338893