Handbook of Machine Learning Applications for Genomics

Discover the groundbreaking insights in the Handbook of Machine Learning Applications for Genomics by Sanjiban Sekhar Roy. Published by Springer Verlag in 2022, this essential hardback edition spans 218 pages and delves into the transformative role of machine learning in genomics.

This comprehensive guide explores the impact of multiomics data analysis on cancer research through tensor decomposition, and highlights innovative machine learning techniques in protein engineering. Additionally, it covers the applications of convolutional neural networks (CNN) in genomics, the challenges posed by long noncoding RNAs in diagnosing human diseases, and the potential of machine learning to revolutionize the future of medicine.

Perfect for researchers, practitioners, and students alike, this handbook is a must-have resource for anyone interested in the intersection of machine learning and genomics.