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
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A Statistical and Machine Learning Perspective
Produktdetaljer
ISBN
9781000425758
Publisert
2021
Utgave
1. utgave
Utgiver
Vendor
Chapman & Hall
Språk
Product language
Engelsk
Format
Product format
Digital bok