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Currently, machine learning is playing a pivotal role in the
progress of genomics. The applications of machine learning are
helping all to understand the emerging trends and the future scope
of genomics. This book provides comprehensive coverage of machine
learning applications such as DNN, CNN, and RNN, for
predicting the sequence of DNA and RNA binding proteins, expression
of the gene, and splicing control. In addition, the book addresses
the effect of multiomics data analysis of cancers using tensor
decomposition, machine learning techniques for protein engineering,
CNN applications on genomics, challenges of long noncoding RNAs in
human disease diagnosis, and how machine learning can be used as
a tool to shape the future of medicine. More importantly, it
gives a comparative analysis and validates the outcomes of machine
learning methods on genomic data to the functional laboratory tests
or by formal clinical assessment. The topics of this book will
cater interest to academicians, practitioners working in the
field of functional genomics, and machine learning. Also, this book
shall guide comprehensively the graduate, postgraduates, and Ph.D.
scholars working in these fields.
This book proposes applications of tensor decomposition to
unsupervised feature extraction and feature selection. The author
posits that although supervised methods including deep learning
have become popular, unsupervised methods have their own
advantages. He argues that this is the case because unsupervised
methods are easy to learn since tensor decomposition is a
conventional linear methodology. This book starts from very basic
linear algebra and reaches the cutting edge methodologies applied
to difficult situations when there are many features (variables)
while only small number of samples are available. The author
includes advanced descriptions about tensor decomposition including
Tucker decomposition using high order singular value decomposition
as well as higher order orthogonal iteration, and train tenor
decomposition. The author concludes by showing unsupervised methods
and their application to a wide range of topics. Allows readers to
analyze data sets with small samples and many features; Provides a
fast algorithm, based upon linear algebra, to analyze big data;
Includes several applications to multi-view data analyses, with a
focus on bioinformatics.
Currently, machine learning is playing a pivotal role in the
progress of genomics. The applications of machine learning are
helping all to understand the emerging trends and the future scope
of genomics. This book provides comprehensive coverage of machine
learning applications such as DNN, CNN, and RNN, for predicting the
sequence of DNA and RNA binding proteins, expression of the gene,
and splicing control. In addition, the book addresses the effect of
multiomics data analysis of cancers using tensor decomposition,
machine learning techniques for protein engineering, CNN
applications on genomics, challenges of long noncoding RNAs in
human disease diagnosis, and how machine learning can be used as a
tool to shape the future of medicine. More importantly, it gives a
comparative analysis and validates the outcomes of machine learning
methods on genomic data to the functional laboratory tests or by
formal clinical assessment. The topics of this book will cater
interest to academicians, practitioners working in the field of
functional genomics, and machine learning. Also, this book shall
guide comprehensively the graduate, postgraduates, and Ph.D.
scholars working in these fields.
This book proposes applications of tensor decomposition to
unsupervised feature extraction and feature selection. The author
posits that although supervised methods including deep learning
have become popular, unsupervised methods have their own
advantages. He argues that this is the case because unsupervised
methods are easy to learn since tensor decomposition is a
conventional linear methodology. This book starts from very basic
linear algebra and reaches the cutting edge methodologies applied
to difficult situations when there are many features (variables)
while only small number of samples are available. The author
includes advanced descriptions about tensor decomposition including
Tucker decomposition using high order singular value decomposition
as well as higher order orthogonal iteration, and train tenor
decomposition. The author concludes by showing unsupervised methods
and their application to a wide range of topics. Allows readers to
analyze data sets with small samples and many features; Provides a
fast algorithm, based upon linear algebra, to analyze big data;
Includes several applications to multi-view data analyses, with a
focus on bioinformatics.
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