Handbook of machine learning applications for genomics / Sanjiban Sekhar Roy, Y.-H. Taguchi, editors.
Roy, Sanjiban Sekhar, editor.; Taguchi, Y.-H., editor.
2022
QH441.2
Available Online

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Title
Handbook of machine learning applications for genomics / Sanjiban Sekhar Roy, Y.-H. Taguchi, editors.
ISBN
9789811691584 (electronic bk.)
9811691584 (electronic bk.)
9811691576
9789811691577
DOI
https://doi.org/10.1007/978-981-16-9158-4
Published
Singapore : Springer, [2022]
Copyright
©2022
Language
English
Description
1 online resource : illustrations (chiefly color).
Item Number
10.1007/978-981-16-9158-4 doi
Call Number
QH441.2
Dewey Decimal Classification
572.8/60285631
Summary
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.
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Access limited to authorized users.
Source of Description
Description based on print version record.
Added Author
Roy, Sanjiban Sekhar, editor.
Taguchi, Y.-H., editor.
Series
Studies in big data ; v. 103.
Available in Other Form
HANDBOOK OF MACHINE LEARNING APPLICATIONS FOR GENOMICS.
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Local and global characterization of genomic data
DNA sequencing using RNN
Deep learning to study functional activities of DNA sequence
Autoencoders for gene clastering
Dimension reduction in gene expression using deep learning
To predict DNA methylation states using deep learning
Transfer learning in genomics
CNN model to analyze gene expression images
Gene expression Prediction using advanced machine learning
Predicting splicing regulation using deep learning
Transcription factor binding site prediction using deep learning
Deep learning for prediction of structural classification of proteins
Prediction of secondary strucure of RNA using advanced machine learning and deep learning
Deep learning for pepositioning of drug and pharmacogenomics.

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