Handbook of Machine Learning Applications for Genomics (Studies in Big Data, 103)

Currently, machine learning is playing a pivotal role in the progress of genomics. Machine learning applications help all understand 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, gene expression, and splicing control. In addition, the book addresses the effect of multi-omics 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 functional laboratory tests or by formal clinical assessment. The topics of this book will cater to the interests of academicians and practitioners working in functional genomics and machine learning. Also, this book will comprehensively guide the graduate, postgraduate, and Ph.D. scholars working in these fields.