Advanced AI Techniques and Applications in Bioinformatics (Smart and Intelligent Computing in Engineering)

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Contributors
Editors
Chapter 1 An Artificial Intelligence-based Expert System for the Initial Screening of COVID-19
	1.1 Introduction
	1.2 Review of Literature
	1.3 Material and Method
		1.3.1 Hierarchical Fuzzy System
		1.3.2 Methodology
	1.4 Results
		1.4.1 Fuzzy Inference System
		1.4.2 Membership Functions
		1.4.3 Rule Editor
		1.4.4 Fuzzification and Defuzzification
		1.4.5 Rule Viewer
		1.4.6 Surface Viewer
		1.4.7 Graphical User Interface
	1.5 Conclusion
	Bibliography
Chapter 2 An Insight into the Potential Role of Artificial Intelligence in Bioinformatics
	2.1 Introduction
	2.2 Artificial Intelligence
		2.2.1 Objectives of AI
	2.3 Need for Integration of AI and Bioinformatics
	2.4 Application of AI in Bioinformatics
		2.4.1 Data and Knowledge Management
		2.4.2 Information Extraction in Biological Literature
		2.4.3 Gene and Noncoding RNA Prediction
		2.4.4 Protein Structure Prediction
		2.4.5 Evolutionary Studies
		2.4.6 Drug Discovery
		2.4.7 Vaccine Development
	2.5 Conclusion and Future Prospects
	Bibliography
Chapter 3 AI-Based Natural Language Processing for the Generation of Meaningful Information Electronic Health Record (EHR) Data
	3.1 Introduction
	3.2 Related Work
	3.3 Artificial Intelligence
	3.4 Machine Learning Overview
		3.4.1 Approaches to Machine Learning
	3.5 Deep Learning Overview
		3.5.1 Multi-Layer Perceptron (MLP)
		3.5.2 Convolutional Neural Networks (CNN)
		3.5.3 Recurrent Neural Networks
		3.5.4 Auto Encoders (AE)
		3.5.5 Restricted Boltzmann Machine (RBM)
	3.6 Natural Language Processing (NLP)
	3.7 Electronic Health Record Systems (EHR)
	3.8 Deep Learning-Based EHR
		3.8.1 EHR Information Extraction in Deep Learning
			3.8.1.1 Concept of Single Extraction
			3.8.1.2 Extraction of Temporal Event
			3.8.1.3 Relation of Extraction
			3.8.1.4 Expansion of Abbreviation
	3.9 Representation of Learning in EHR
	3.10 Methods of Evaluation for EHR Representation Learning
		3.10.1 Outcome Prediction in EHR Representation Learning
	3.11 The Case for NLP Systems as an EHR-Based Clinical Research Tool
		3.11.1 Use Cases for NLP System in Asthma Research
	3.12 Implications of NLP for EHR Based on Clinical Research and Care
	3.13 Conclusion and Future Directions
	Bibliography
Chapter 4 AI and Genomes for Decisions Regarding the Expression of Genes
	4.1 Introduction to Artificial Intelligence (AI)
	4.2 AI in Clinical Genomics
		4.2.1 Variant Calling
		4.2.2 Variant Classification and Annotation of Genomes
			4.2.2.1 Coding Mutants/Variants
			4.2.2.2 Non-Coding Mutants/Variants
	4.3 AI in Gene Expression Data Analysis
		4.3.1 Dimensionality Reduction
			4.3.1.1 Feature Extraction
			4.3.1.2 Feature Selection
			4.3.2 Clustering
			4.3.3 Bayesian Networks
	4.4 Conclusion
	Bibliography
Chapter 5 Implementation of Donor Recognition and Selection for Bioinformatics Blood Bank Application
	5.1 Introduction
		5.1.1 About Software Application Development
		5.1.2 About the Blood Bank at JPMC
			5.1.2.1 Process for Blood Collection
			5.1.2.2 Process for Blood Issuance
		5.1.3 Biometrics-AI application
		5.1.4 What Is Fingerprinting
		5.1.5 Problem Statement
	5.2 Literature Review
		5.2.1 Data Collections and Interviews
		5.2.1.1 Blood Compatibility
	5.3 Methodology
		5.3.1 Bioinformatics Blood Bank Application Framework
		5.3.2 System Analysis
		5.3.3 Gathering Information
			5.3.3.1 Observation
			5.3.3.2 Record Review
		5.3.4 System Design
		5.3.5 Software Environment
		5.3.6 AI-Software Application Platform
		5.3.7 Database Management System (DBMS)
		5.3.8 Reporting Environment
			5.3.8.1 Crystal Reports
			5.3.8.2 SQL Server Reporting Services (SSRS)
		5.3.9 Hardware and Software Environment
		5.3.10 System Development
		5.3.11 Database Design
		5.3.12 Alpha Testing
		5.3.13 Beta Testing
		5.3.14 Test Deliverables
	5.4 Result and Discussion
		5.4.1 Relationships
		5.4.2 Normalization
		5.4.3 Code Design
	5.5 Conclusion
		5.5.1 Implementation and Evaluation
		5.5.2 Results
		5.5.3 Limitations
		5.5.4 Conclusion
		5.5.5 Extensibility
	Bibliography
	Appendix
	Application Process Flow
Chapter 6 Deep Learning Techniques for miRNA Sequence Analysis
	6.1 Introduction
		6.1.1 Biogenesis of miRNA
		6.1.2 Biology behind miRNA-Target (mRNA) Interactions
	6.2 miRNA Sequence Analysis
	6.3 Deep Learning: Conceptual Overview
		6.3.1 Deep Neural Networks (DNNs)
		6.3.2 Convolutional Neural Networks (CNNs)
		6.3.3 Autoencoders
		6.3.4 Recurrent Neural Networks (RNNs)
		6.3.5 Long Short-Term Memory (LSTM)
	6.4 Deep Learning: Applications for Pre-miRNA Identification
	6.5 Deep Learning: Applications for miRNA Target Prediction
	6.6 Critical Observations and Future Directions
	6.7 Conclusion
	Bibliography
Chapter 7 Role of Machine Intelligence in Cancer Drug Discovery and Development
	7.1 Introduction
	7.2 Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in Drug Discovery and Development
	7.3 Challenges to Overcome
	7.4 Future Prospects and Conclusion
	Abbreviations
	Bibliography
Chapter 8 Genome and Gene Editing by Artificial Intelligence Programs
	8.1 Introduction
	8.2 Genome Sequencing and Editing
		8.2.1 The Gene-Editing Opportunities and Threats
	8.3 Personalized Therapy and Life-Saving Services in the Context of Genome Editing
		8.3.1 Genome Editing Initiative and Pharmaceuticals Implementation for Designing Drugs
	8.4 CRISPR and Genome Editing
		8.4.1 Genome Editing for the Next Generation in the Context of Medicine
	8.5 Epigenetic and Germline Editing Positions
	8.6 Natural Science in the Context of Artificial Intelligence Platform
	8.7 Optimization of Human Bio-Machinery
	8.8 Genomics Is Revolutionized by Artificial Intelligence
	8.9 Artificial Intelligence Captaincy and Frontiers for the Direct Competitors of Genomics
	8.10 Artificial Intelligence and Gene Editing Mechanisms
	8.11 Scientists Used AI to Improve Gene-Editing Accuracy
		8.11.1 Gene Editing and Biomedical Engineering Systems
		8.11.2 Training Models for Gene Editing in the Context of Machine Learning
		8.11.3 Off-Target Scores
		8.11.4 End-to-End Guide Design
	8.12 Efficient Genome Editing and the Optimization of Humans’ Health
	8.13 Further Research and Development for Gene Editing with AI
		8.13.1 Regulatory Considerations of Gene Editing and Artificial
 Intelligence
	8.14 Policies and Recommendations of Genome Editing with AI
	8.15 Conclusion
	8.16 Future Prospects of AI
	Bibliography
Chapter 9 Artificial Neural Network (ANN) Techniques in Solving the Protein Folding Problem
	9.1 Introduction
	9.2 Role of Molten Globules in Protein Folding
	9.3 Importance of Protein Folding Studies
	9.4 Concept of Artificial Neural Networks
	9.5 Criteria and Evaluation of Applications of ANN in the Protein Folding Problem
	9.6 Bio-Inspired Optimization Algorithms that Can Be Used for Protein Folding Study in Association with ANN
	9.7 Implementation of Artificial Neural Network Methods in Protein Folding Studies
	9.8 Limitations of Current Protein Folding Prediction Algorithms
	9.9 Conclusion
	Bibliography
Chapter 10 Application of Machine Learning and Molecular Modeling in Drug Discovery and Cheminformatics
	10.1 Introduction
	10.2 Machine Learning Methods in Cheminformatics
		10.2.1 Machine Learning Platforms
		10.2.2 Representation of Small Molecules
		10.2.3 Training Set Creation
		10.2.4 Model Evaluation Methods
		10.2.5 Model Evaluation Metrics
		10.2.6 Feature Reduction
	10.3 Molecular Modeling Methods in Cheminformatics
		10.3.1 Virtual Screening
		10.3.2 Pharmacophore Modeling
		10.3.3 Molecular Docking
		10.3.4 Molecular Simulation Approach to Drug Screening
	10.4 Conclusion and Future Directions
	Bibliography
Chapter 11 Role of Advanced Artificial Intelligence Techniques in Bioinformatics
	11.1 Introduction
	11.2 Bioinformatics: Analyzing Life Data at the Molecular Level
		11.2.1 DNA
		11.2.2 RNA
		11.2.3 Proteins
		11.2.4 Glycans
	11.3 Application of AI in Bioinformatics
	11.4 Symbolic Machine Learning
		11.4.1 Nearest Neighbor Approaches in Bioinformatics
		11.4.2 Application in Viral Protease Cleavage Prediction
	11.5 Neural Networks in Bioinformatics
	11.6 Evolutionary Computation in Bioinformatics
	11.7 Deep Learning in Informatics
	11.8 Future Trends
	11.9 Conclusion
	Bibliography
Chapter 12 A Bioinformatics Perspective on Artificial Intelligence in Healthcare and Diagnosis: Applications, Implications, and Limitations
	12.1 Introduction
	12.2 The Data Overload
	12.3 Big Healthcare Data
		12.3.1 Big Data from Electronic Health Records
		12.3.2 Big Data from Omics
		12.3.3 Big Data from Medical Images
		12.4 Data Preprocess and Data Integration
	12.5 Data Exploration
		12.5.1 Artificial Intelligence in Clinical Diagnostics
		12.5.2 Artificial Intelligence in EHR-Based Diagnostics
		12.5.3 Artificial Intelligence in Image-Based Clinical Diagnostics
		12.5.4 Artificial Intelligence in Genomics-Based Clinical Diagnostics
	12.6 Machine Learning in Cancer Prognosis
	12.7 Limitations of Artificial Intelligence in Healthcare
		12.7.1 Data Dependency and Inconsistent Data
		12.7.2 Infrastructure Requirements
		12.7.3 Data Privacy and Security
	12.8 Discussion
	Bibliography
Chapter 13 Accelerating Translational Medical Research by Leveraging Artificial Intelligence: Digital Healthcare
	13.1 Introduction
		13.1.1 Origin of Artificial Intelligence
		13.1.2 A.I. and Big Data
		13.1.3 Optimizing the Machine–Human Interface
		13.1.4 Role of Artificial Intelligence in Clinical Research
		13.1.5 The Core Elements of Smart Healthcare Communities (S.H.C)
	13.2 Related Study
		13.2.1 Problem Statement
		13.2.2 Technology Support: Proposed Solution
		13.2.3 Research Challenges/Gaps and Infrastructure Requirements
	13.3 Methodology: Phases Involved in the Adoption of A.I. for Translation Research
	13.4 Approved Proprietary A.I. Algorithms
	13.5 Digital Transformation and Interoperability
	13.6 Limitations and Future Perspectives
	13.7 Key-Points Drawn
	13.8 Conclusion
	Bibliography
Index