Computational Techniques for Biological Sequence Analysis [Hardcover]

This book provides an overview of basic and advanced computational techniques for analysing and understanding protein, RNA, and DNA sequences. This book acts as useful reference for bioinformaticians and computational biologists working in the field of molecular biology, genomics, and bioinformatics.

About the Editors. Contributors. Chapter 1 Machine Learning and Computational Models for the Prediction of Post-Translational Modification Sites. Chapter 2 Application of Artificial Intelligence in Recognition of Gene Regulation and Metabolic Pathways. Chapter 3 Assessment of Machine Learning Algorithms in DNA Sequence Data Mining. Chapter 4 Efficient Detection and Recuperation of Mental Health using X (Formerly Twitter) and Fitbit Data-Based Recommendation System. Chapter 5 Role of Artificial Intelligence in Detection of Congenital Diseases. Chapter 6 A Hybrid Multi-Level Segmentation-Based Ensemble Classification Model. Chapter 7 Innovative Approaches to Bilirubin Detection. Chapter 8 Targeted Immunization: Application of Machine Learning in Prediction of IL-4 Inducing Peptides. Chapter 9 Healthcare Portal-Django Framework for Healthcare Management System. Chapter 10 Harnessing Machine Learning and Deep Learning for DNA Sequence Analysis. Index.

This book provides an overview of basic and advanced computational techniques for analyzing and understanding protein, RNA, and DNA sequences. It covers effective computing techniques for DNA and protein classifications, evolutionary and sequence information analysis, evolutionary algorithms, and ensemble algorithms. Furthermore, the book reviews the role of machine learning techniques, artificial intelligence, ensemble learning, and sequence-based features in predicting post-translational modifications in proteins, DNA methylation, and mRNA methylation, along with their functional implications. The book also discusses the prediction of proteinprotein and proteinDNA interactions, ls2