Translational Bioinformatics Applications in Healthcare

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Preface
Editors
Contributors
Part I: translational Healthcare, Next-Generation Sequence analysis, and Drug repurposing
	Chapter 1 Translational Healthcare System through Bioinformatics
		1.1 Introduction
		1.2 Data and Biomedicine
		1.3 Genomics and Bioinformatics
		1.4 Pharmacogenomics
		1.5 Mechanism of PG
			1.5.1 Pharmacokinetics
			1.5.2 Pharmacodynamics
		1.6 PG in Drug Discovery
		1.7 Drug Discovery and Development through Omics Technologies
			1.7.1 Omics Technology
		1.8 Availability of Omics Technology in Drug Discovery and Development
		1.9 Role of Genomics in Drug Discovery and Development
		1.10 Role of Transcriptomics in Drug Discovery and Development
		1.11 Role of Proteomics in Drug Discovery and Development
		1.12 Artificial Intelligence and Machine Learning of Drugs
		1.13 Real time Biomedical and Healthcare Data
		1.14 Biomedical and Healthcare Challenges
		1.15 Opportunities to Improve Biomedical and Healthcare Sectors
		1.16 Data models for Healthcare and Well Care Analytics
		1.17 Conclusion and Future Prospects
		Acknowledgments
		References
	Chapter 2 Next-Generation Sequence Analysis for Clinical Applications
		2.1 Introduction
			2.1.1 First-Generation Sequencing
			2.1.2 Second-Generation Sequencing
			2.1.3 Next-Generation Sequencing
		2.2 Clinical Applications
			2.2.1 Cancer
			2.2.2 Epigenetic Diseases
			2.2.3 Mendelian Diseases
			2.2.4 Infectious Diseases
		2.3 The Scope of NGS Technology in Clinical Environment
		2.4 Proceedings of NGS Methods in Clinical Application
		2.5 Validation of the Results Obtained
		2.6 Limitations and Future Prospects
		2.7 Conclusion
		References
	Chapter 3 Clinical Applications of Next-Generation Sequence Analysis in Acute Myelogenous Leukemia
		3.1 Introduction
		3.2 Background and History of Sequencing
		3.3 Acute Myelogenous Leukemia
			3.3.1 AML and Its Morphology
			3.3.2 Classification Systems for AML
			3.3.3 Cytogenetics
			3.3.4 Molecular Abnormalities in AML
			3.3.5 Prognosis/Risk Stratification
			3.3.6 Therapeutics for AML Patients
				3.3.6.1 Strategies of Consolidation
			3.3.7 Novel Targets for AML
				3.3.7.1 FLT3 Inhibitors
				3.3.7.2 Isocitrate Dehydrogenase Inhibitors
				3.3.7.3 Nuclear Exporter Inhibitors
				3.3.7.4 Immunological Therapies
		3.4 Next-Generation Sequencing Technologies
			3.4.1 Terminology and Its Recent Developments
			3.4.2 How NGS Is Going from Research to Clinical Settings: An Overview
			3.4.3 Clinical Applications of NGS Technologies in Cancer Research
				3.4.3.1 Whole-Genome Sequencing
				3.4.3.2 Transcriptome Sequencing
				3.4.3.3 Whole-Exome Sequencing
				3.4.3.4 Targeted Next-Generation Sequencing
				3.4.3.5 Other Applications of NGS
		3.5 NGS Analysis Guidelines for the Diagnosis of AML
		3.6 How AML Genes Implicate Mutations? A Prognostic and Therapeutic Overview
			3.6.1 Understanding Cytogenetic and Molecular Genetic Mutations in AML
			3.6.2 How Are Novel Somatic Mutations in AML Identified and Characterized?
			3.6.3 How Are Cancer-Susceptible Genes in AML Identified?
			3.6.4 How Are Cryptic Translocations in Complex Genomes Characterized in AML?
		3.7 Challenges in the Diagnosis of AML using NGS
			3.7.1 Biological-Clinical Challenges
			3.7.2 Technical Challenges
			3.7.3 Other Challenges
		3.8 Future of NGS in Cancer Genomes
		3.9 Conclusion
		References
	Chapter 4 Translational Bioinformatics Methods for Drug Repurposing
		4.1 Introduction
			4.1.1 Drug Repurposing
				4.1.1.1 Drug Repurposing Profiles
			4.1.2 Databases/Repositories for Drug Repurposing
				4.1.2.1 Usage of Above Repositories in Drug Repurposing
			4.1.3 Computational Approaches for Drug Repurposing
				4.1.3.1 Literature Mining Approach
				4.1.3.2 Network-Based Approach
			4.1.4 Web-Based Tools for Drug Repurposing
				4.1.4.1 ACID
				4.1.4.2 repoDB
				4.1.4.3 RE:FineDrugs
				4.1.4.4 ReDO_DB
				4.1.4.5 RepurposeDB
				4.1.4.6 DeepDR
				4.1.4.7 Drug Vs Disease/DvD
			4.1.5 Summary
		References
Part II: Internet of things, Viroinformatics, and toxin Databases for Healthcare applications
	Chapter 5 The Fundamentals and Potential of IoT for Bioinformatics and Healthcare
		5.1 Introduction
		5.2 Translational Medicine
		5.3 Biotechnology for Translational Research with a Focus on Human healthcare
		5.4 Translational Medicine’s Focus on Interdisciplinary Gap
		5.5 IoT in Bioinformatics and Biotechnology
		5.6 IoT in Healthcare
			5.6.1 IoT in Healthcare Applications
		5.7 Discussion and Recent Trends
			5.7.1 Recent Trends
		5.8 Conclusion
		References
	Chapter 6 Viroinformatics and Viral Diseases: A New Era of Interdisciplinary Science for a Thorough Apprehension of Virology
		6.1 Introduction
		6.2 Virology: An Overview
			6.2.1 Brief History
			6.2.2 Structure, Function, and Classification of Viruses
			6.2.3 Frequent Outbreaks of Viral Diseases
		6.3 What Is Viroinformatics?
			6.3.1 Viroinformatics-Based Databases and Tools
				6.3.1.1 Viroinformatical Databases
				6.3.1.2 Viroinformatical Tools
		6.4 Viroinformatics: Need of the Hour
			6.4.1 Future Perspectives of Viroinformatics
		6.5 Conclusion
		References
	Chapter 7 Toxin Databases and Healthcare Applications
		7.1 Introduction
			7.1.1 Plant Toxin
			7.1.2 Bacterial Toxin
			7.1.3 Animal Toxin
		7.2 Toxins in Healthcare
			7.2.1 Drug Discovery
			7.2.2 Cosmetics
		7.3 Toxins Databases
			7.3.1 Toxic Exposome Database T3DB
			7.3.2 Database Kalium
			7.3.3 Animal Toxin Databases (ATDB)
			7.3.4 Toxin Plants Phytotoxin Database (TPTT)
			7.3.5 ArachnoServer Spider Toxin Database
			7.3.6 Toxin Antitoxin Database (TADB)
			7.3.7 RISCTOX Database
			7.3.8 DBETH Bacterial Exotoxins for Humans
			7.3.9 Super Toxic Database
			7.3.10 TAS Mania Bacterial Toxin-Antitoxin (TA Database)
		7.4 Conclusion
		Acknowledgments
		References
Part III: Medical Image Processing and Other Healthcare applications
	Chapter 8 Lossless Medical Image Compression Using Hybrid Block-Based Algorithm for Telemedicine Application
		8.1 Introduction
		8.2 Technical Background
			8.2.1 Overview of Wavelet Transform
			8.2.2 Overview of Hadamard Transform
		8.3 Materials and Methods
			8.3.1 DC Prediction for LL Sub-Band Coding
			8.3.2 Transformation for Non-LL Sub-Band Coding
			8.3.3 IWT-LHT-Huffman Encoding
			8.3.4 IWT-LHT-Arithmetic Encoding
			8.3.5 Algorithm
		8.4 Results and Discussion
		8.5 Conclusion
		References
	Chapter 9 Improved FCM Based on Gaussian Kernel and Crow Search Optimization for ROI Extraction on Corona Virus Disease (COVID-19) CT Images
		9.1 Introduction
		9.2 Improved ARKFCM Based on Crow Search Optimization
			9.2.1 Crow Search Optimization
			9.2.2 Improved Adaptive Regularized Kernel FCM with Crow Search Algorithm
		9.3 Results and Discussion
		9.4 Conclusion
		References
	Chapter 10 Estimating the Effect of Social Distancing in the Progression Dynamics of COVID-19
		10.1 Introduction
		10.2 Material and Methods
			10.2.1 Mathematical Model
			10.2.2 Basic Reproduction Number
		10.3 Sensitivity Analysis of R[sub(0)]
		10.4 Results and Discussion
		10.5 Conclusion
		Acknowledgment
		References
Index