Gene Expression Data Analysis: A Statistical and Machine Learning Perspective

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Book Title
Gene Expression Data Analysis: A Statistical and Machine Learning
Publication Date
2021-11-22
ISBN
9780367338893
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關於產品

Product Identifiers

Publisher
CRC Press LLC
ISBN-10
0367338890
ISBN-13
9780367338893
eBay Product ID (ePID)
22050069979

Product Key Features

Number of Pages
360 Pages
Publication Name
Gene Expression Data Analysis
Language
English
Subject
Computer Science, General, Life Sciences / Biology
Publication Year
2021
Type
Textbook
Subject Area
Computers, Health & Fitness, Science
Author
Pankaj Barah, Dhruba Kumar Bhattacharyya, Jugal Kumar Kalita
Format
Hardcover

Dimensions

Item Weight
30.4 Oz
Item Length
9.2 in
Item Width
6.1 in

Additional Product Features

Intended Audience
College Audience
LCCN
2021-012278
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
572.865
Table Of Content
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.
Synopsis
The book introduces phenomenal growth of data generated by increasing numbers of genome sequencing projects and other throughput technology-led experimental efforts. It provides information about various sources of gene expression data, and pre-processing, analysis, and validation of such data., 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 simultaneously monitoring the expression patterns of thousands of genes. Data produced from such experiments are voluminous (both in dimensionality and numbers of instances) and evolving in nature. Analysis of huge amounts of data toward 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 with a multidisciplinary 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 and practical knowledge of methods for identifying 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 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 multidisciplinary researchers and practitioners in computer science and biological sciences, 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.
LC Classification Number
QH450.B38 2022

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