Roy Sanjiban Sekhar, Taguchi Y.-H. (eds.) Handbook of Machine Learning Applications for Genomics

Springer, 2022. — 222 p. — (Studies in Big Data 103). — ISBN: 978-981-16-9157-7.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 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 for 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.Multiomics Data Analysis of Cancers Using Tensor Decomposition and Principal Component Analysis Based Unsupervised Feature Extraction.
Machine Learning for Protein Engineering.
Statistical Relational Learning for Genomics Applications:​ A State-of-the-Art Review.
A Study of Gene Characteristics and Their Applications Using Deep Learning.
Computational Biology in the Lens of CNN.
Leukemia Classification Using Machine Learning and Genomics.
In Silico Drug Discovery Using Tensor Decomposition Based Unsupervised Feature Extraction.
Challenges of Long Non-Coding RNAs in Human Disease Diagnosis and Therapies:​ Bio-Computational Approaches.
Protein Sequence Classification Using Convolutional Neural Network and Natural Language Processing.
Machine Learning for Metabolic Networks Modeling:​ A State-of-the-Art Survey.
Single Cell RNA-seq Analysis Using Tensor Decomposition and Principal Component Analysis Based Unsupervised Feature Extraction.
Machine Learning:​ A Tool to Shape the Future of Medicine.