Translational Informatics: Prevention and Treatment of Viral Infections (Advances in Experimental Medicine and Biology, 1368)

"This book introduces the translational informatics applied to most aspects of virus infection, including tracking of virus origin, detection and prevention of infection, drug discovery, and vaccine design as well as smart city-level monitoring and controlling of the virus epidemic by government. It covers the informatics for data mining and modelling at molecular, tissue/organ, individual, and population levels. The informatics for immunological mechanisms and the personalized prediction and treatment of infected patients are also summarized. The perspectives on the application of artificial intelligence to the prevention of virus outbreaks are also given. This book will be helpful to readers who are interested in prevention of virus infection, biomedical informatics, and artificial intelligence in medicine and healthcare."--Page 4 de la couverture Contents About the Editor 1 Databases, Knowledgebases, and Software Tools for Virus Informatics 1.1 Introduction 1.2 Databases and Knowledgebases for Computational Virus Research 1.2.1 Data Resources for Multiple Virus Groups 1.2.2 Virus Type-Specific Data Platforms 1.3 Bioinformatics Models and Tools for Translational Virus Analysis 1.3.1 Multiple Sequence Alignment 1.3.2 Phylogenetic and Evolutionary Understanding 1.3.3 Genome-Wide Exploration 1.4 Data-Driven and AI-Assisted Studies for the Control of Viral Infection 1.4.1 SARS-CoV-2 1.4.2 HBV/HCV 1.4.3 Influenza Virus 1.5 Virus Informatics: From Virus Surveillance to Health Promotion 1.5.1 Opportunities for Precision Virus Management and Systems Healthcare 1.5.2 Challenges and Perspectives 1.6 Conclusions References 2 Detection and Prevention of Virus Infection 2.1 Introduction 2.2 Virus Detection and Prevention 2.2.1 Influenza Virus Detection and Prevention 2.2.1.1 Cell Culture-Based Detection 2.2.1.2 RIDTs 2.2.1.3 Immunofluorescence Assays 2.2.1.4 Serological Assays 2.2.1.5 NATs 2.2.1.6 Influenza Virus Prevention 2.2.2 Coronavirus Detection and Prevention 2.2.2.1 Coronavirus Detection 2.2.2.2 Coronavirus Prevention 2.2.3 HIV and HTLV-1 Detection and Prevention 2.2.3.1 HIV and HTLV-1 Detection 2.2.3.2 HIV and HTLV-1 Prevention 2.2.4 HPV Detection and Prevention 2.2.4.1 HPV Detection 2.2.4.2 HPV Prevention 2.2.5 Herpes Virus Detection 2.2.5.1 Herpes Virus Detection 2.2.5.2 Herpes Virus Prevention 2.2.6 Hepatitis Virus Detection and Prevention 2.2.6.1 Hepatitis Virus Detection 2.2.6.2 Hepatitis Virus Prevention 2.2.7 Arbovirus Detection and Prevention 2.2.7.1 Arbovirus Detection 2.2.7.2 Arboviruses Prevention 2.2.8 Filovirus Detection and Prevention 2.2.8.1 Filovirus Detection 2.2.8.2 Filovirus Prevention 2.2.9 Rabies Virus Detection and Prevention 2.2.9.1 Rabies Virus Detection 2.2.9.2 Rabies Virus Prevention 2.3 Informatics for Detection and Prevention of Virus Infection 2.3.1 Gene Regulatory Network Modeling and Biomarker Prediction Based on Multi-omics Data 2.3.2 Integrative Analysis and Classification Prediction Based on Clinical Indicators and Demographic Information 2.3.3 Features Extraction and Analysis Based on Viral Genome Sequences 2.3.4 Image Classification Strategies Based on the Deep Learning Model 2.3.5 Knowledge Discovery via Text Mining in Electronic Medical Records 2.3.6 Drug Discovery 2.4 Conclusions and Perspective References 3 Bioinformatics for the Origin and Evolution of Viruses 3.1 Introduction 3.2 Informatics for Tracing the Origin of Viruses 3.2.1 Mechanism of Virus Dissemination 3.2.2 Zoonotic Origin or Accidental Laboratory Escape? 3.2.3 Phylogenetic Inference of Virus Origin 3.2.4 Virus Originated from Different Geographical Locations 3.3 Prediction of SARS-CoV-2 Evolution 3.3.1 Data Resources for Virus Mutations 3.3.2 Characterization of Virus Mutations 3.3.2.1 Mutation Detection at Sequence Level 3.3.2.2 Recombination Analysis 3.3.2.3 Mutation Detection at Structural Level 3.3.3 Evolutionary Analysis of Virus Spike Protein and Host Cell Receptor ACE2 3.3.4 Mutation and Virulence Analysis 3.3.5 Mutation Constraints and Drug/Vaccine resistance 3.3.6 Prediction of the Fitness of the Virus Mutations 3.3.7 Long-Term Evolution and Herd Immunity 3.4 Conclusions References 4 In Silico Drug Discovery for Treatment of Virus Diseases 4.1 Introduction 4.2 In Silico Drug Designing: Concepts and Methods 4.2.1 2D/Descriptor-Based Approach 4.2.2 3D/Conformation-Based Approach 4.2.3 Structure-Based Drug Design (SBDD) 4.2.3.1 Threading and Homology Modeling for SBDD 4.2.3.2 De Novo SBDD 4.2.3.3 Structure-Based Virtual Screening (VS) for SBDD 4.2.4 Ligand-Based Drug Design (LBDD) 4.2.4.1 Quantitative Structure-Activity Relationship (QSAR) for LBDD 4.2.4.2 Pseudoreceptor Modeling for LBDD 4.2.4.3 Pharmacophore Mapping/Modeling for LBDD 4.2.4.4 Scaffold Hopping for LBDD 4.3 The Journey of In Silico Drug Discovery and Design for the Treatment of Viral Diseases 4.4 Conclusion References 5 Vaccines and Immunoinformatics for Vaccine Design 5.1 Introduction 5.2 Concept of Immunome-Derived Vaccines 5.3 The Journey of Vaccine Development Through Genome to Immunome 5.3.1 Antigen Presentation, and Activation and Generation of T- and B-Cell Immune Responses 5.4 Comparison of the Genome Sequences from Pathogens for Vaccine Development: A Novel Approach 5.5 Designing Vaccines Using Immunoinformatics Tools 5.6 Advanced Immunoinformatics Tools for Epitope Mapping 5.7 What Makes Immunoinformatics Tools so Proficient at Identifying Critical Antigens/T-Cell Epitopes for Vaccines? 5.8 Immunoinformatics: A Boon for Vaccine Design and Development 5.8.1 In Silico Vaccine Design and Development 5.8.1.1 Microarray-Based Vaccine Design 5.8.1.2 Epitope-Based Vaccine Design 5.8.1.3 Peptide-Based Approach for Vaccine Design 5.8.1.4 Non-alignment-Based Vaccine Design 5.8.1.5 Designing DNA Vaccines 5.8.2 Modeling and Simulation of Immunological Responses 5.9 Designing Vaccines Using Immunoinformatics Tools: Pitfalls and Future Interventions 5.10 Conclusion References 6 Predicting the Disease Severity of Virus Infection 6.1 Introduction 6.2 Informatics for Diagnosis of COVID-19 with Varying Severity 6.2.1 Different SARS-CoV-2 Strains and Infection Severity 6.2.2 Clinical Features Associated with COVID-19 Severity 6.2.2.1 Demographics 6.2.2.2 Hematological and Biochemical Parameters 6.2.2.3 Comorbidities 6.2.2.4 Radiographic Features 6.2.3 Cytokine Storm for Classification of COVID-19 Severity Prediction 6.2.4 Biomarkers for Prediction of COVID-19 Severity 6.3 Models for Classification and Prediction of COVID-19 Severity 6.3.1 Scores or Indexes for COVID-19 Severity Measurement at the Individual Level 6.3.2 Mathematical Models for Population Level Prediction of the COVID-19 Severity 6.3.3 Artificial Intelligence (Machine Learning or Deep Learning) Models for Classification of COVID-19 Severity 6.4 Summary and Perspectives References 7 Modeling the Virus Infection at the Population Level 7.1 Introduction 7.2 Model Form 7.2.1 Caputo Fractional Order Ordinary Differential Equation 7.2.2 Caputo Fractional Order Model of the Virus Infection at the Population Level 7.3 Qualitative Analysis 7.3.1 Uniqueness 7.3.2 Invariant Set 7.3.3 Stability 7.4 Numerical Modeling 7.4.1 Genetic Algorithm 7.4.2 Setup 7.4.3 Implementation Appendix References 8 Health-Based Geographic Information Systems for Mapping and Risk Modeling of Infectious Diseases and COVID-19 to Support Spatial Decision-Making 8.1 Introduction 8.2 Environmental Distribution of Infectious Disease and GIS-Related Research 8.2.1 Use of Spatial Clustering and Spatial Statistics in Identifying Disease Hotspots 8.2.2 Use of Spatial Interpolation in Estimating Disease Pattern 8.2.3 Spatial Visualization and Web-Based GIS Dashboard 8.2.4 Exploring Environmental and Social Factors Using Spatial Regression Analysis 8.3 Human-Centered Efforts to Address COVID-19 Challenges 8.3.1 Early in the Pandemic: Contact Tracing and Initial Control 8.3.2 During Control Measures: Compliance Monitoring 8.3.3 Reopening: When, How, and Where 8.3.4 Post-Pandemic: Recovery and Transition Gauging 8.4 Conclusion and Discussion References 9 5G, Big Data, and AI for Smart City and Prevention of Virus Infection 9.1 Introduction 9.2 5G and Big Data Promote the AI and Smart City*-6pt 9.2.1 5G and Big Data 9.2.2 Artificial Intelligence 9.2.3 The Development of Smart City*-6pt 9.2.3.1 ICT, the Internet of Things, and Smart City 9.2.3.2 Smart City and Its Components 9.2.3.3 Smart City Related Technologies and Applications 9.3 From AI for XAI: Challenges and Opportunities 9.4 Smart City and Prevention of Virus Infection 9.4.1 Virus Spread Process and Corresponding Responses 9.4.2 Smart Applications for Virus Prevention 9.4.2.1 Smart Applications for Clinical Screening, Diagnosis, Classification, and Treatment 9.4.2.2 Smart Applications for Information Tracking, Information Coordination and Disease Outbreak Prediction 9.4.2.3 Smart Applications for Information Screening and Public Awareness Monitoring Based on Social Media 9.4.2.4 Smart Applications for Supply Chain Support 9.5 Summary and Perspectives References Index