Roy S. Handbook of Machine Learning Applications...Genomics 2022 torrent

Textbook in PDF format

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.
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
Leukaemia 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 Modelling:​ A State-of-the-Art Survey
Sincle 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