Nature-Inspired Intelligent Computing Techniques in Bioinformatics

Preface
About This Book
Contents
Editor and Contributors
Preliminaries
The Scope and Applications of Nature-Inspired Computing in Bioinformatics
	1 Introduction
	2 Nature-Inspired Models of Computation
		2.1 Traditional NIC Models
		2.2 New NIC Models
	3 Scope and Applications of NIC Models in Bioinformatics
	4 Limitations of Nature-Inspired Computing Models in Bioinformatics
	5 Future Prospects
	6 Conclusion
	References
Leveraging Healthcare System with Nature-Inspired Computing Techniques: An Overview and Future Perspective
	1 Introduction
	2 Nature-Inspired Algorithm: An Overview
		2.1 Swarm Intelligence
		2.2 Biology-Inspired Algorithm
		2.3 Physics-Based Algorithm and Chemistry-Based Algorithm
	3 Combining AI Technologies and Nature-Inspired Computing Models for COVID-19
	4 Nature-Inspired Algorithms and Their Significant Use in Health Care
	5 Healthcare Management
	6 Radiology
		6.1 Radiotherapy
		6.2 Orthopedics
	7 Obstetrics and Gynecology
	8 Oncology
	9 Cardiology
	10 Endocrinology
	11 Pharmacotherapy
	12 Neurology
	13 Surgery
	14 Infectious Disease
	15 Rehabilitation Medicine
	16 Artificial Bee Algorithm
		16.1 Biomedical Sensors
		16.2 Noise Filtering and Cancellation
		16.3 DNA Microarray
		16.4 Confirmatory Diagnosis
		16.5 EEG Analysis
	17 Ant Colony Optimization
		17.1 Fast Medicine Dispensing System (FMDS)
		17.2 Drug Management in Hospitals and Pharmacies: OPM
	18 Conclusion
	References
Nature-Inspired Computing in Cancer Research
Nature-Inspired Computing in Breast Cancer Research: Overview, Perspective, and Challenges of the State-of-the-Art Techniques
	1 Introduction
	2 Nature-Inspired Computing Solutions for Breast Cancer
	3 Nature-Inspired Algorithms
		3.1 Genetic Algorithm
		3.2 Ant Colony Optimization
		3.3 Artificial Bee Colony Optimization
		3.4 Swarm Optimization Algorithms
	4 Choice of Fitness Functions and Parameter Setting
		4.1 Parameter Tuning and Fitness Functions for ACO
		4.2 Parameter Tuning and Fitness Function for ABC
		4.3 Parameter Tuning and Fitness Function for Firefly Algorithm
	5 Challenges of NIC Techniques in Breast Cancer Research
	6 Future Research Direction
	7 Conclusion
	References
Advances in Genomic Profiling of Colorectal Cancer Using Nature-Inspired Computing Techniques
	1 Introduction
	2 Landscape of Genomic Alterations in CRC
		2.1 Molecular Subtypes of CRC
	3 Overview of Computational Intelligence-Based Models Used in CRC Diagnosis and Their Limitations
	4 Impact of Nature-Inspired Computing in Cancer Research
	5 Implementation of NIC Methods in CRC Detection
		5.1 Genetic Algorithm (GA)
		5.2 Ant Colony Optimization (ACO)
		5.3 Particle Swarm Optimization (PSO)
		5.4 Artificial Bee Colony (ABC)
		5.5 Cuckoo Search Algorithm (CSA)
	6 Application of NIC Techniques in the Identification of Potential Biomarkers in CRC
	7 Case Studies of Various Hybrid Techniques: A Revolutionary Change in CRC Detection and Treatment
	8 Smartphone Technology in Diseases Monitoring: An Intelligent Approach
	9 Advantages, Limitations, and Constraints of NIC Techniques in Cancer Research
	10 Conclusion and Future Perspective
	References
Potential Role of the Nature-Inspired Algorithms for Classification of High-Dimensional and Complex Gene Expression Data
	1 Introduction
	2 Nature-Inspired Algorithms in Gene Expression Classification
		2.1 Cuckoo Search Algorithm
		2.2 Multi-objective Cuckoo Search Algorithm
		2.3 Artificial Bee Colony (ABC)
		2.4 AdaBOOST Algorithm
		2.5 Support Vector Machines
		2.6 Boruta Algorithm
		2.7 K-Nearest Neighbour Algorithm
		2.8 Random Forest Algorithm
	3 Pros and Cons of Nature-Inspired Algorithms for Gene Classification
	4 Future Prospects
	5 Conclusion
	References
Optimized Nature-Inspired Computing Algorithms for Lung Disorder Detection
	1 Introduction
		1.1 Introduction on Nature-Inspired Optimization Algorithms
		1.2 Common Characteristics
	2 Literature Survey on Lung Disorder Classifications
	3 Contribution of the Chapter
	4 Proposed Model
	5 Results
	6 Conclusion
	References
Overview and Classification of Swarm Intelligence-Based Nature-Inspired Computing Algorithms and Their Applications in Cancer Detection and Diagnosis
	1 Introduction
	2 Classification of Nature-Inspired Computing Algorithms
		2.1 Evolutionary-Based Nature-Inspired Computing Algorithms
		2.2 Ecology-Based Nature-Inspired Algorithms
		2.3 Multi-Objective Nature-Inspired Algorithms
		2.4 Swarm Intelligence-Based Nature-Inspired Algorithms
	3 NIC Techniques (Insect-Based) to Diagnose Human Disease
	4 Role of ABC, ACO, GSO, ALO, and FA in the Diagnosis of Cancers
	5 Limitations
	6 Future Directions
	7 Conclusion
	References
Nature-Inspired Computing: Scope and Applications of Artificial Immune Systems Toward Analysis and Diagnosis of Complex Problems
	1 Introduction
	2 Nature-Inspired Computing
	3 Bio-inspired Algorithms
		3.1 Algorithms Based on Evolution
		3.2 Algorithms Based on Swarm Intelligence
		3.3 Algorithms Based on Ecology
		3.4 Multi-Objective Algorithm
	4 Artificial Immune System
		4.1 Biological Aspects Focused on Developing the Algorithm
		4.2 Clonal Selection Theory
		4.3 Immune Networks
		4.4 Negative Selection
		4.5 Artificial Immune System Algorithms
	5 Artificial Immune System Applications
		5.1 Fault Detection, Diagnosis, and Recovery Using AIS
		5.2 Immune Network-Based Approaches
		5.3 Automobile Hydraulic Brake Fault Diagnosis
		5.4 Fault Detection in Refrigerator System
		5.5 Fault Diagnosis, Detection, and Isolation of Wind Turbine
		5.6 Fault Diagnosis in Transformer
	6 Conclusion
	References
Nature-Inspired Computing: Bat Echolocation to BAT Algorithm
	1 Introduction
	2 Discovery of Echolocation
	3 Vocalization in Bats
	4 The BAT Algorithm
		4.1 Drawbacks
		4.2 New Variants and Application
	5 Conclusion
	References
Social, Emotional and Ethical (SEE) Attributes, Which Configures Our Bioinformatics Systems to Activate the Hidden Forces to Shape Human Decisions
	1 Introduction
	2 The Three Domains and Three Dimensions
		2.1 Compassion
		2.2 Awareness
		2.3 Engagement
	3 The Three Domains Are Three Types of Things
		3.1 The Personal Domain
		3.2 The Social Domain
		3.3 The Systems Domain
	4 Literature Review
		4.1 How to Take a Decision in a Multi-Solution Environment?
	5 Mathematical Modulation
	6 Model of Decision-Making
		6.1 The Personalization
		6.2 The Present Situation
	7 Process of Decision-Making
	8 Equations for Decision-Making Models
		8.1 Formulation of Human Behaviour into Mathematical Equations
		8.2 Predictably Irrational Behaviour
		8.3 Why Can’t We Just Force Ourselves to Do What We Want?
		8.4 Why Do We Place an Excessive Value on Our Possessions?
		8.5 Why Do Options Cause Us to Lose Sight of Our Main Goal?
		8.6 Why Do We Get What We Expect from Our Minds?
		8.7 Why Can a 50-Cent Aspirin Perform Functions that a Penny Aspirin Cannot?
		8.8 Why Are We Untrustworthy and What Can We Do About It?
		8.9 Why Do We Become More Honest When We Deal with Cash?
	9 START-Action STRATEGY (SAS)
	10 Conclusion and Future Prospects
	References
Nature-Inspired Computing in Drug Design, Development, and Therapeutics
Applications of Nature-Inspired Computing and Artificial Intelligence Algorithms in Solving Personalized Therapy Complications
	1 Introduction
	2 NIC and AI Algorithms Used in Personalized Medicine
	3 Nature-Inspired Algorithms
		3.1 Genetic Algorithm (GA)
		3.2 Ant Colony Optimization (ACO)
		3.3 Particle Swarm Optimization (PSO)
		3.4 Artificial Bee Colony (ABC)
		3.5 Firefly Algorithm (FA)
	4 Artificial Intelligence Tools
		4.1 Artificial Neural Network (ANN)
		4.2 Naive Bayesian (NB)
		4.3 Support Vector Machines (SVM)
		4.4 Fuzzy Logic (FL)
	5 NIC and AI in Personalized Medicine: Advantages and Limitations
	6 Challenges of NIC and AI in Personalized Medicine
	7 Future Prospective
	8 Conclusion
	References
Role of Nature-Inspired Intelligence in Genomic Diagnosis of Antimicrobial Resistance
	1 Introduction
	2 AMR Diagnosis: Genome-Based Techniques as a Rescue
	3 Nature-Inspired Intelligence
		3.1 Artificial Neural Network
		3.2 Artificial Immune Systems
		3.3 Bio-Inspired Swarm Optimization Algorithms
		3.4 Particle Swarm Optimization
		3.5 Bee Colony Optimization
		3.6 Ant Colony Optimization
	4 Big Data Resources
		4.1 Genomic Databases
		4.2 Antimicrobial Resistance Databases
	5 Implementation of ML for Genomic Prediction of AMR
	6 Development of Machine Learning Model
	7 Challenges and Future Perspectives
	8 Conclusion
	References
Consequential Innovations in Nature-Inspired Intelligent Computing Techniques for Biomarkers and Potential Therapeutics Identification
	1 Introduction
	2 Nature-Inspired Intelligent Computing Techniques in Biomarkers Prediction
		2.1 Skin Cancer
		2.2 Breast Cancer
		2.3 Colon Cancer
		2.4 Alzheimer’s and Heart Diseases
		2.5 Blood Infections
	3 Classification of Nature-Inspired Computation
		3.1 Swarm Intelligence
		3.2 Natural Evolution
		3.3 Artificial Neural Network
		3.4 Molecular Biology
		3.5 DNA Computing
		3.6 Biological Cells
	4 Mathematical Methods in Biological Processes
		4.1 Kolmogorov Equations
	5 Current Trends in Nature-Inspired Intelligent Computing Techniques for Biomarker and Drug Target Identification
	6 Software and Tools
		6.1 BioDiscML
		6.2 Psipred 4.0
	7 Advantages of Nature-Inspired Intelligent Computing Techniques
	8 Limitations and Future Directions
		8.1 Limitations
		8.2 Future Directions
	9 Conclusion
	References
Nature-Inspired Computing Techniques in Drug Design, Development, and Therapeutics
	1 Introduction
	2 Nature-Inspired Intelligent Computing Tools
	3 MicroRNA-Disease Association Prediction Method-Tuned with Surrogate Model Assisted EA
		3.1 MiRAI Tuned with Surrogate Model
		3.2 Applications and Future Prospective
	4 Nature-Inspired Computing Models Toward Accurate Detection of COVID-19
	5 Role of PSO Techniques in Medical Disease Diagnosis
		5.1 The Technique—Particle Swarm Optimization (PSO)
		5.2 PSO in Disease Identification
	6 Nature-Inspired Chemical Reaction Optimization Algorithms
		6.1 Variants of CRO
	7 Cuckoo Search Method for Drug Design and Discovery in Chemoinformatics Using Hybrid Harris Hawks’ Optimization
	8 Conclusion
	References
Illustrious Implications of Nature-Inspired Computing Methods in Therapeutics and Computer-Aided Drug Design
	1 Introduction
	2 Historical Background of NIC Techniques
		2.1 Nature-Inspired Techniques in Ancient Therapeutics to Drug Designing
	3 Advance Computation and NIC Techniques
		3.1 Swarm Intelligence
		3.2 Biological Cells
		3.3 Biological Neural Network
	4 NIC in Therapeutics and Computer-Aided Drug Designing
		4.1 Virtual Connectional Libraries
		4.2 NIC Techniques for Therapeutics and Computer-Aided Drug Designing
		4.3 Molecular Docking and Dynamics Simulations
		4.4 Pharmacophore Modelling
	5 Advantages of Implementing the NIC Techniques
	6 Challenges of NIC Techniques in Biomedical Computation
	7 Future Perspective and Scope of NIC Techniques in Therapeutics and Drug Design
	8 Conclusion
	References
Nature-Based Computing Bioinformatics Approaches in Drug Discovery Against Promising Molecular Targets Carbonic Anhydrases and Serine/Threonine Kinases for Cancer Treatment
	1 Introduction
	2 Computer-Aided Drug Design for Tumor-Associated Human Carbonic Anhydrases
	3 Computer-Aided Drug Design for Tumor-Associated Serine/Threonine Protein Kinase
	4 Limitation of Nature-Based Computing Bioinformatics Approaches in Drug Discovery
	5 Conclusion
	References