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.

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.