Epigenetics and Its Role in Human Cancer

Abstract

Includes index. This book offers a detailed overview of translational bioinformatics together with real-case applications. Translational bioinformatics integrates the areas of basic bioinformatics, clinical informatics, statistical genetics and informatics in order to further our understanding of the molecular basis of diseases. By analyzing voluminous amounts of molecular and clinical data, it also provides clinical information, which can then be applied. Filling the gap between clinic research and informatics, the book is a valuable resource for human geneticists, clinicians, health educators and policy makers, as well as graduate students majoring in biology, biostatistics, and bioinformatics. Preface; Contents; Part I: Computer-Aided Drug Discovery; Chapter 1: Drug Discovery; 1.1 Introduction; 1.1.1 Need for Drug Design; 1.2 Target Identification; 1.3 Computer-Aided Drug Design; 1.3.1 Structure-Based Drug Design (SBDD); 1.3.1.1 Molecular Docking; 1.3.1.2 Types of Docking; 1.3.1.3 Scoring Functions; 1.3.1.4 Limitations and Challenges; 1.3.2 Ligand-Based Drug Design (LBDD); 1.3.2.1 Pharmacophore Modelling; Structure-Based Pharmacophore Modelling; Ligand-Based Pharmacophore Modelling; 1.3.2.2 Virtual Screening; Lipinski Rule. 1.3.2.3 Quantitative Structure-Activity Relationship (QSAR)1.3.3 Illustrated Examples Using CADD; 1.4 Clinical Trials; 1.4.1 Preclinical Trials; 1.4.2 Human Clinical Trials; 1.4.3 Types of Clinical Trials; 1.5 Conclusions; Appendices; Appendix I; Docking Algorithms; Appendix II; Virtual Screening Methods: Appendix II; References; Chapter 2: Translational Bioinformatics and Drug Discovery; 2.1 Introduction; 2.1.1 Translational Bioinformatics; 2.2 Supporting Resources; 2.2.1 Online Database; 2.2.2 Small Chemical Structure Database; 2.3 Chemical Data Mining Strategies; 2.4 Genomic Technologies. 2.4.1 Next-Generation Sequencing (NGS)2.4.2 NGS and Personalized Medicine; 2.5 Structure-Based Drug Discovery; 2.5.1 Molecular Docking; 2.6 Ligand-Based Drug Discovery; 2.6.1 Quantitative Structural Activity Relationship (QSAR); 2.6.1.1 Model Development; 2.6.1.2 Data Analysis Method; 2.6.1.3 Regression Method; 2.6.1.4 2D QSAR (Girgis et al. 2015); 2.6.1.5 QSAR Model Validation; 2.7 Pharmacokinetic and Pharmacodynamic (PKPD) Simulation (Nielsen and Friberg 2013); 2.7.1 Pharmacokinetics; 2.7.2 Pharmacodynamics; 2.8 Conclusion; References. Chapter 3: Translational Research in Drug Discovery and Development3.1 Translational Research; 3.1.1 How Is It Different from Traditional Research?; 3.1.2 Translation Continuum from Benchside to Bedside; 3.1.3 Translational Research Phases; 3.1.4 Translational and Clinical Science; 3.1.5 Reengineering Translational Science; 3.1.6 Opportunities in Translational Research; 3.1.6.1 Opportunities for Researchers; 3.1.6.2 Opportunities for Institutions; 3.1.7 Challenges in Translational Research; 3.1.8 Controversies in Translational Research; 3.2 Translational Drug Discovery. 3.2.1 Translational Drug Development for Diseases3.2.1.1 Nervous System Disorders; 3.2.1.2 Psychiatric Disorders; 3.2.1.3 Cancer; 3.3 Strategies to Accelerate Translational Research in Drug Development; 3.3.1 Prioritizing Area of Research and Objectives; 3.3.2 Meaningful Collaboration; 3.3.3 Technology Upgradation; 3.3.4 Bridging Interventional Development Gaps (BrIDGs) Scheme; 3.3.5 Drug Repurposing; 3.3.5.1 Computational Chemistry; 3.3.5.2 Literature Mining; 3.3.5.3 Genome-Wide Association Study (GWAS); 3.4 Conclusion; References. Drug Discovery -- Translational Bioinformatics and Drug Discovery -- Translational Research in Drug Discovery and Development -- Exploring the Potential of Herbal Ligands towards Multidrug Resistant Bacterial Pathogens by Computational Drug Discovery -- The Progress of New Targets of Anti-HIV and Its Inhibitors -- Exploration of Drug Candidates Interacting on Amyloid-β Protofibrils for Alzheimer's Disease -- Homology Modeling, Structure Based Pharmacophore Modeling, High Throughput Virtual Screening and Docking Studies of L-Type Calcium Channel for Cadmium Toxicity -- Natural Compounds Are Smart Player in Context to Anticancer Potential: An In Silico and In Vitro Advancement -- Genome Wide Association Studies -- A Survey of Bioinformatics Based Tools in RNA-Sequencing (RNA-Seq) Data Analysis -- Epigenetics and Its Role in Human Cancer -- Methods for Microbiome Analysis -- Pharmacogenomics: Clinical Perspective, Strategies and Challenges -- Computational Network Approaches and Their Applications for Complex Diseases -- Bioinformatics Applications in Clinical Microbiology -- Artificial Intelligence and Automatic Image Interpretation in Modern Medicine -- Computation in Medicine: Medical Image Analysis and Visualization.