Bioinformatics Tools and Big Data Analytics for Patient Care

Cover
Half Title
Title Page
Copyright Page
Table of Contents
List of Figures
List of Tables
Preface
Acknowledgments
Editors’ Biographies
List of Contributors
Chapter 1: The Role of Bioinformatics Tools and Technologies in Clinical Trials
1.1 Introduction
1.2 Clinical Research
1.2.1 Pre-clinical Investigations
1.2.2 Stage 0
1.2.3 Stage I
1.2.4 Stage II
1.2.5 Stage III
1.2.6 Stage IV
1.3 The Role of (BI) Tools and Technologies in Clinical Research
1.3.1 Identification of Target
1.3.1.1 Computerized Technique for Site Determination
1.3.1.1.1 Bioinformatic Modeling
1.3.1.1.2 Polypeptide Dynamics
1.3.1.1.3 Expression Proteomics
1.3.1.1.4 Target Confirmation
1.3.2 Datasets and Computational Devices Utilized for Target Approval
1.3.2.1 Gene logic
1.3.2.2 Ribonucleic Interference Technology
1.3.2.3 Immusol
1.3.2.4 Aptamers
1.3.3 Lead Identification/Optimization
1.4 Management of Clinical Data (CDM)
1.4.1 E-Clinical Solutions
1.4.2 Oracle Clinical Data Collection, as well as Oracle Remote Data Capture
1.4.3 Electronic Case Report Form
1.5 Utilization of BI
1.6 BI Tools and Databases
1.6.1 Online Mendelian Inheritance in Man (OMIM)
1.6.2 3D Tooth Atlas
1.6.3 Cardio source Plus
1.6.4 RD-CONNECT
1.6.5 EVIMalaR
1.6.6 Florinash
1.6.7 Chernobyl Tissue Bank (CTB)
1.7 Data Management in BI
1.7.1 Data Mining
1.8 Challenges and Opportunities
1.9 Conclusion
References
Chapter 2: Bioinformatics Tools and Software in Clinical Research
2.1 Bioinformatics
2.2 BI Applications
2.2.1 Sequence Analysis
2.2.2 Protein Framework Prediction
2.2.3 Annotation of the Genome
2.2.4 Comparative Genomics
2.2.5 Drug Discovery and Health
2.2.6 Development of COVID-19 Vaccine
2.3 Tools of BI
2.3.1 Sequence Analysis and Gene Identification
2.3.1.1 Basic Local Alignment Search Tool (BLAST)
2.3.1.2 FASTA
2.3.1.3 HMMER
2.3.1.4 European Molecular Biology Open Suite Software (EMBOSS)
2.3.1.5 THREADER
2.3.1.6 Clustral Omega
2.3.1.7 SEQUEROME
2.3.1.8 ProtParam
2.3.1.9 WebGeSTer DB
2.3.1.10 GENSCAN
2.3.2 Phylogenetic Investigations
2.3.3 Protein Structure and Function Predication
2.3.4 Drug Discovery
2.4 Drug Development
2.4.1 Preclinical Research
2.4.2 Clinical Studies
2.5 New Approaches
2.6 Development of a Drug Database
2.7 BI and Healthcare Informatics
2.8 Software for Clinical Trials
2.8.1 Electronic Data Capture (EDC)
2.8.2 Remote Data Capture (RDC)
2.8.3 Oracle Clinical
2.8.4 eCRF
2.9 Drug Research and Development
2.9.1 Target Identification
2.9.1.1 Molecular Docking
2.9.1.2 Proteomics
2.9.2 Target Validation
2.9.2.1 Gene Logic
2.9.2.2 Ribonucleic Interference Technology
2.9.2.3 Immusol
2.9.2.4 Aptamers
2.9.3 Lead Identification/Optimization
2.9.3.1 Comprehensive Chemistry that Is Health
2.9.3.2 The Drug Bank
2.9.3.3 PharmaGKB
2.9.4 Pre-clinical Development
2.9.5 Clinical Trials
2.10 Pharmacovigilance
2.11 Software Found in Pharmacovigilance
2.11.1 Oracle Argus Safety
2.11.1.1 Ensures Global Regulatory Compliance
2.11.1.2 Offers Better Data Insights and Faster Decision-making
2.11.1.3 Integrates Risk and Safety Management
2.11.1.4 Proven and Accepted in the Business
2.11.2 ARISg
2.11.3 Oracle AERS
2.11.4 PvNET
2.11.5 repClinical
2.12 Conclusion
Acknowledgment
References
Chapter 3: Computational Biology for Clinical Research
3.1 Introduction
3.2 Software Used in Computational Biological Studies
3.3 Computational Biology Methods
3.3.1 Basic Probabilistic Approaches
3.3.2 Basic Deterministic Approaches
3.3.3 Graphical Approaches
3.3.4 Symbolic Approaches
3.3.5 Mechanistic Approaches
3.4 Applications of Computational Biology in Clinical Research
3.4.1 Computational Analysis of Different Biological Levels (Molecular, Cellular, Tissue/Consortia Level)
3.4.2 Analysis of the Interface of Biotic and Abiotic Processes
3.4.3 Processing of Large Data Sets for Enhanced Analysis
3.4.4 Selection of Parameters and Their Optimization
3.4.5 For Systems Analysis and Systems Biology Study
3.4.6 For Systems Approach in Trauma
3.5 Conclusion
Acknowledgment
References
Chapter 4: Issues and Challenges Related to Clinical Bioinformatics Tools for Clinical Research
4.1 Introduction
4.2 ICT Facilities for Sustaining Medical Bioinformatics
4.3 Specify Data Information Management
4.4 Administration of Information Traceability
4.5 Decisions of Metadata
4.6 Synchronization of Information and Internet Safety
4.7 Information Safety
4.7.1 Confidentiality
4.7.2 Personal Privacy
4.7.3 Security
4.8 Sharing Data Information
4.8.1 Data Information Sharing Needs an Information Environment
4.8.2 Promptness, Visibility, and Effectiveness Are of Significance for Information Carriers
4.8.3 Browsing and Removal of Appropriate Information from Huge Information Repositories
4.9 Collectively Develop an Experiment
4.10 Management of Ranges and Assumptions
4.11 Difficulties in Efficient CBT Advancement and Circulation in the Academic Community
4.12 Training
4.13 Conclusions
Acknowledgment
References
Chapter 5: Artificial Intelligence: An Emerging Technique in Pharmaceutical and Healthcare Systems
5.1 Introduction
5.2 Types of AI
5.2.1 Artificial Intelligence: Type 1
5.2.1.1 Narrow AI (Weak AI)
5.2.1.2 General AI
5.2.1.3 Super AI
5.2.2 Artificial Intelligence: Type 2
5.2.2.1 Reactive Machines
5.2.2.2 Limited Memory
5.2.2.3 Theory of Mind
5.2.2.4 Self-awareness
5.3 AI Timeline in Healthcare
5.4 AI Technologies Used in Healthcare
5.4.1 Machine Learning
5.4.2 Deep Learning
5.4.3 Machine Vision
5.4.4 Natural Language Processing
5.4.5 Robotics in Healthcare
5.5 Role of AI in Patient Monitoring
5.5.1 The Modern Health Care Era
5.5.2 Precision Medicine
5.5.3 Intelligent Personal Health Records
5.5.4 Robotics and AI Powered Devices
5.6 AI in Cancer Treatment
5.6.1 Applications of AI in Oncology Are
5.6.2 Benefits of Using AI in Cancer Therapy
5.6.3 Cancer Genomics
5.7 AI in Healthcare: Current Challenges
5.7.1 Ethical challenges
5.7.2 Challenges to Doctors Under Clinical Practice
5.7.3 SocioCultural Impacts of AI
5.7.4 AI Security
5.8 The Future of AI in Patient Monitoring and Healthcare
5.9 Conclusion
Acknowledgment
References
Chapter 6: Artificial Intelligence in Healthcare and Its Application in Brain Stroke Diagnosis
6.1 Introduction
6.2 Regulatory Framework for AI in Healthcare
6.2.1 North America
6.2.2 Asia–Pacific (APAC) Region
6.3 Applications of AI in the Healthcare Space
6.3.1 Medical Imaging
6.3.2 Virtual Patient Assistants
6.3.3 Other Real-world Applications
6.3.4 Google’s Deep Mind Instant Alerting Tool for Acute Kidney Injury
6.4 What AI Can Do for Stroke Patients
6.5 Discussion
6.6 Conclusion
References
Chapter 7: Computational Cloud Infrastructure for Patient Care
7.1 Introduction
7.2 Cloud Computing
7.2.1 Basic Models of Cloud Computing: Deployment Models
7.2.2 Basic Models of Cloud Computing: Service Models
7.3 Cloud Computing in Patient Care
7.3.1 Features of Cloud Computing in Healthcare
7.3.2 Benefits of Cloud Computing in Patient Care
7.4 Applications of Cloud Computing in Patient Care
7.4.1 Information Systems
7.4.2 Telemedicine Practices
7.4.3 Digital Libraries
7.4.4 Virtual Medical Universities
7.4.5 Clinical Decision Support System
7.4.6 Population Health Management
7.4.7 Patient Management Practices
7.4.8 Health Education
7.4.9 Biological Software
7.4.10 Drug Discovery
7.5 Issues in Employing Cloud Computing in Healthcare
7.5.1 Technological Issues
7.5.2 Security and Privacy Issues
7.5.3 Legal Issues
7.5.4 Management Issues
7.5.5 Organizational Issues
7.5.6 Environmental Issues
7.5.7 Human Issues
7.6 Regulatory Aspects for Cloud Computing in Patient Care
7.6.1 US Standards
7.6.2 International Standards
7.7 Service Providers for the Healthcare Sector
7.8 Conclusion
Acknowledgment
References
Chapter 8: Advancement in Gene Delivery: The Role of Bioinformatics
8.1 Introduction
8.2 The Conceptual Vision of Bioinformatics
8.3 The Goals of Bioinformatics
8.4 Biological Databases
8.5 Database Sequence Filtering
8.6 Searching a Database
8.7 Databases of Protein Structure
8.7.1 Classification of Protein Structure
8.7.2 Modeling of Structure
8.8 Source of Protein Structure Data
8.9 Classification of Databases
8.10 Features of Bioinformatics
8.11 Initiative for Parallel Bioinformatics (IPAB)
8.12 Software Tools for Bioinformatics
8.12.1 The Various Types of Tools Used in Bioinformatics
8.12.2 Traditional Instrument Bioinformatics
8.12.3 Tools for Analyzing at Different Levels
8.12.4 Gene Prediction Software
8.12.4.1 GRAIL
8.12.4.2 A Genetic Marker
8.13 Gene Finding Methods
8.13.1 Method of Sequencing Alignment
8.13.2 Multiple Sequence Alignment
8.13.3 Phylogenetic Investigation
8.13.4 Protein Structure and Property Prediction
8.14 Bioinformatics Methodologies
8.15 Genomics and Bioinformatics Medical Applications
8.16 Comparative Genomics
8.17 Gene Delivery Objectives
8.17.1 The Gene Delivery Process Using Bioinformatics Tools
8.17.2 Using Bioinformatics Techniques to Modify Genes
8.17.2.1 Gene Substitution
8.17.2.2 Genetic Alteration
8.17.2.3 Genetic Augmentation
8.18 Gene Delivery System
8.19 Using Bioinformatics Technologies to Develop a Gene Delivery System for Therapy
8.19.1 Cancer
8.19.2 Immunological Strategy
8.19.3 Cardiovascular Disorders
8.20 Conclusion
References
Chapter 9: Drug Development Using Cloud Application
9.1 History of Cloud Computing
9.2 Aim of Cloud Computing
9.3 Drug Development Using Cloud Computing
9.4 Definition of Cloud Computing
9.5 Cloud Computing Classifications
9.5.1 SAAS (Software as a Service)
9.5.2 PAAS (Platform-as-a-Service)
9.5.3 IAAS (Infrastructure-as-a-Service)
9.6 Four Categories of Cloud Transmitting Models
9.6.1 Private Cloud
9.6.2 Public Cloud
9.6.3 Community Cloud
9.6.4 Hybrid Cloud
9.7 Concept of the Drug Development Process
9.8 Drug Discovery
9.8.1 Drug Discovery Pathway
9.8.2 Pathophysiology of Diseases
9.9 Role of Cloud Computing in Drug Development
9.10 Cloud-based Tools
9.10.1 Collaborative Drug Discovery (CDD)
9.10.2 In Silico Drug Discovery
9.10.3 Free Energy Perturbation (FEP)
9.11 Cloud Computing Role in Drug Repositioning
9.12 Drug Monitoring with Integrated Cloud Computing
9.13 High Throughput Screening (HTS)
9.14 Introduction to High-Performance Computing (HPC)
9.15 Main Application of Cloud Computing in Drug Development
9.15.1 Virtual Screening and Docking Using Cloud Computing
9.15.2 Structure-based Drug Discovery with Virtual Screening
9.15.3 Small Molecule Virtual Screening
9.15.3.1 New Trends in Virtual Screening
9.16 High Content Screening (HCS)
9.17 Next Generation Sequencing (NGS)
9.18 Cloud Computing for Comparative Genomics
9.19 Cloud Platforms for Genomics
9.19.1 Google Genomics
9.19.2 DNAnexus
9.19.3 Microarrays
9.20 Amazon Elastic Cloud Computing
9.21 Discovery Cloud
9.22 Big Data
9.23 Preclinical Clinical Development Using Cloud Computing
9.24 Benefits of Cloud Computing
9.25 Limitations
9.26 Future Prospects for Cloud Computing in Drug Development
9.27 Conclusion
References
Chapter 10: Cloud Application in Drug Development
10.1 Introduction
10.2 What is Cloud?
10.3 Key Benefits of Cloud Computing
10.4 Market Size and Forecast
10.5 Market Segmentation
10.6 Growth Drivers and Challenges
10.7 Cloud-based Solutions
10.7.1 How Cloud Computing Improves upon Traditional Methods
10.7.1.1 Scalable, Cost-effective Clinical Research Services
10.7.1.2 A Novel Approach to Drug Design and Discovery
10.7.1.3 Speeding the Discovery of Novel Drug Candidates
10.7.1.4 Accurately Predicting Ligand Binding
10.7.1.5 Casting the Net into Molecular Space
10.7.1.6 Quantum Molecular Design Workflow
10.7.1.6.1 Preclinical Development
10.7.1.6.2 Clinical Trials
10.7.1.7 De novo Design in the Cloud
10.7.2 Cloud-Based Services
10.8 Conclusion
Acknowledgment
References
Chapter 11: Framework for Handling Medical Data in Research
11.1 Introduction
11.2 Definitions
11.3 Health Data Characterizations
11.4 Big Data Characteristics
11.5 Big Data Analytics
11.6 Big Data in Healthcare
11.7 Challenges in Big Data Analytics
11.8 Big Data Security and Privacy
11.9 Effect of Big Data on the Healthcare System
11.10 Issues in Healthcare Big Data
11.11 Recording and Analysis of Big Data
11.12 The Fruitful Use of the Medical Database
11.13 Data Analysis Assistance in Clinical Trials
11.14 Securing Medical Data
11.15 Healthcare and Big Data Simpatico
11.16 Small Data vs. Big Data: Why Do Clinicians Prefer Small Data?
11.17 Innovation in Medical Services and Small Data
11.17.1 Predictions Made by Patients
11.17.2 Electronic Health Records
11.17.3 Real Time
11.17.4 Enhancing Patient Commitment
11.17.5 Opioid Abuse Prevention
11.17.6 Informed Strategic Planning Using Health Data
11.17.7 Big Data Has the Potential to Cure Cancer
11.17.8 Predictive Analytics
11.17.9 Decrease Fraud and Improve Security
11.17.10 Telemedicine
11.17.11 Incorporating Big-Style Data
11.17.12 How to Stop Unnecessary Emergency Room Visits
11.17.13 Smart Staffing and Personnel Management
11.17.14 Discovery and Progress
11.17.15 Advanced Disease and Risk Management
11.17.16 Suicide and Self-Harm
11.17.17 Improved Supply Chain Management
11.17.18 Researching and Developing New Therapies and Inventions
11.18 High-Cost Patients
11.19 Big Data Triage
11.20 Adverse Events
11.21 Diseases that Affect Several Organ Systems
11.21.1 Research
11.21.2 Discussion and Future Work
11.22 Conclusion
References
Index