Handbook of Machine Learning Applications for Genomics (Hardcover, 1st ed. 2022)

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.

General

Imprint:	Springer Verlag, Singapore
Country of origin:	Singapore
Series:	Studies in Big Data, 103
Release date:	June 2022
First published:	2022
Editors:	Sanjiban Sekhar Roy • Y-H. Taguchi
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	218
Edition:	1st ed. 2022
ISBN-13:	978-981-16-9157-7
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Books > Science & Mathematics > Biology, life sciences > Life sciences: general issues > Genetics (non-medical) > General
LSN:	981-16-9157-6
Barcode:	9789811691577

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