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Full Description
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 protein-protein and protein-DNA interactions, protein structure, and function using computational methods. It also presents techniques for quantitative analysis of protein-DNA interactions and protein methylation and their involvement in gene regulation. Additionally, the use of nature-inspired algorithms to gain insights into gene regulatory mechanisms and metabolic pathways in human diseases is explored. This book acts as a useful reference for bioinformaticians and computational biologists working in the fields of molecular biology, genomics, and bioinformatics.
Key Features:
Reviews machine learning techniques for DNA sequence classification and protein structure prediction
Discusses genetic algorithms for analyzing multiple sequence alignments and predicting protein-protein interaction sites
Explores computational methods for quantitative analysis of protein-DNA interactions
Examine the role of nature-inspired algorithms in understanding the gene regulation and metabolic pathways
Covers evolutionary algorithms and sequence-based features in predicting post-translational modifications
Contents
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.
