The book will play a vital role in improvising knowledge on the practical application of information science in the biological field to a great extent. All the researchers and practitioners will benefit from those working in Big Data, IoT, Computational Intelligence, biomedical, and bioinformatics. This book would be a good collection of state-of-the-art approaches for data mining based on bioinformatics and health-related applications. It will be very beneficial for the new researchers and practitioners working in the field to follow the best-performing methods quickly. They would be able to compare different approaches and carry forward their research in the most critical area of research that directly impacts the betterment of human life and health. This book would also be instrumental because no text in the market provides a good collection of state-of-the-art methods of big data-driven bioinformatics. While emerging technology has made data entry much more accessible, it will also discuss challenges for researchers and medical professionals. Data sets have grown so huge that extracting and analyzing data with traditional methods has become challenging. However, these data sets also give an exciting opportunity to understand large-scale patterns and make predictions about health care. Intelligent technologies have employed the knowledge and implementation of big data techniques globally. This book also proposes Computational Approaches for Vaccine Designing, which will help us better understand ourselves and our environment. This book references new information processing technology that combines ideas from biology, chemistry, and medical science to manage electronic medical records productively. Specific topics covered include database management, genomics, proteomics, and scalability.
Author(s): Sujata Dash, Hrudayanath Thatoi, Subhendu Kumar Pani, Seyedamin Pouriyeh
Series: Computer Science, Technology and Applications
Publisher: Nova Science Publishers
Year: 2023
Language: English
Pages: 404
City: New York
Contents
Preface
Overview
Objective
Organization
Audiences for the Publication
Abbreviations
Part I: Application and Analysis of Omics Data
Chapter 1
The Value of Next-Generation Sequencing and Multi-Omics Data for Clinical Diagnosis: Future Perspectives on Breast Cancer
Abstract
Introduction
NGS Types, Workflow, and Its Potency in Cancer Research and Clinical Routine
Driver and Passenger Mutations in Cancer, and Methods for Differentiating In-between
Strategies Relying on Differences in Biological Feature
Strategies Relying on Machine-Learning Methods
Methods Relating to the Effect of Mutations on Their Functional Properties
Pathway-Based and Network-Based Analysis
Next-Generation Sequencing in the Era of Breast Cancer Researches
Multi-omics Data in Cancer Research
Omics Data Repositories Databases
Catalogue of Somatic Mutations in Cancer (COSMIC)
Proteomics Identification Database (PRIDE)
Genomic Expression Omnibus (GEO)
Conclusion
References
Chapter 2
Next-Generation Sequencing and Omics Data Analysis Techniques
Abstract
Introduction
Recent Advances in the Application of Next-Generation Sequencing and Omics Data Analysis Techniques
Conclusion
References
Chapter 3
In silico Approaches to Vaccine Design
Abstract
Introduction
In silico Approaches for Vaccine Designing
Numerous In silico Approaches for Vaccine Designing
Conclusion
References
Chapter 4
Evolution of Genomic Medicine
Abstract
Introduction
Methodological Advancement
DNA, the Genetic Material
High Throughput Sequencing Technology
Pre-Genomic Era
First Generation Sequencing
Second Generation Sequencing
Third Generation Sequencing
Computational Tools Development
Whole Genome Analysis Tools
Disease and Drug Target Tools
Evolutionary Studies
Structural Modelling
Advent of Human Genome Projects
A Brief History
Significant Outcomes
ENCODE
GENCODE
Development of Genomic Database
Primary Databases
Secondary Databases
Composite Databases
Genome Annotation and Comparison
Paradigm Shift: Traditional to Genome Medicine
Need of Genome Medicine: Broader Vision
Genetic Diversity
Shortcomings of Traditional Medicine
GWAS and QTL Mapping
Implementation of Genome Medicine
Success, Challenges and Opportunities
Cancer
Sickle Cell Anaemia
Lactose Intolerance
Genomic Medicine and Its Financial Impact
Ethical and Legal Issues
Genome Medicine: Hope or Hype
Conclusion
References
Chapter 5
Bio-Inspired Computing
Abstract
Introduction
P-System
Informational Description
Components of the P-System
The Environment
Membranes
Symbols
Catalysts
Rules
Computation Process
Rule Application
Non-Deterministic Application
Maximally Parallel Application
As a Computation Model
Computation
Step 1
Step 2
Step 3
Step 4
Step 5
Step 6
Computation Halts
Bio-Inspired Swarm Optimization Algorithms
Genetic Bee Colony (GBC) Algorithm
Setting ABC Parameter
Initialization of the Population of Solutions
Evaluation of the Population Solutions
Employer Bee
Onlooker Bee
Scout Bee
Genetic Operators
Cat Swarm Optimization (CSO)
Seeking Mode
The Tracing Mode
Artificial Algae Algorithm (AAA)
Helical Movement Phase
Evolutionary Process Phase
Adaptation Phase
Elephant Search Algorithm (ESA)
Chicken Swarm Optimization (CSO)
Behavioral Understanding
Mathematical Understanding
Grey Wolf Optimization (GWO) Algorithm
Mathematical Understanding
Moth–Flame Optimization (MFO) Algorithm
Algorithm
Generating the Initial Population of Moths
Updating the Positions of Moths
Updating the Number of Flames
Different Variants of MFO
Multi-objective
Binary
Hybridization
Applications
Whale Optimization Algorithm (WOA)
Mathematical Model
Bubble-net Attacking Method (Exploitation Phase)
Shrinking Encircling Mechanism
Spiral Updating Position
Search for Prey (Exploration Phase)
Fish Swarm Optimization Algorithm (FSOA)
Concept and Algorithm
Individual Movement Operator
Food Operator
Instinctive Collective Movement Operator
Non-Instinctive Collective Movement Operator
Artificial Neural Network
Artificial Bee Colony Algorithm
Cuckoo Optimization Algorithm (COA)
Bacterial Foraging Optimization Algorithm (BFOA)
Flower Pollination Algorithm (FPA)
Neuromorphic Engineering
Neurological Inspiration
Neuromorphic Prototypes
Neuromorphic Sensors
Conclusion
References
Chapter 6
Feature Selection and Classification of Microarray Cancer Dataset: Review and Challenges
Abstract
Introduction
Microarray Technology
Feature Selection
Methods
Filter
Wrapper
Embedded
Hybrid
Classification
Logistic Regression
Naïve Bayes
K-Nearest Neighbor (KNN)
Support Vector Machine
Random Forest
Decision Trees
Dataset
Related Work
Performance Evaluation Measures
Result and Analysis
Based on the Following
Feature Selection
Dataset
Classifier
Conclusion
References
Part II: Application of Bioinformatics Tools and Databases
Chapter 7
Machine Learning Methods in Bioinformatics
Abstract
Introduction
Recent Trends in the Application of Machine Learning in Bioinformatics Techniques
Conclusion
References
Chapter 8
Molecular Biomarkers as Health and Disease Predictors
Abstract
Introduction
Specific Authors That Have Worked on Molecular Biomarkers as Health and Disease Predictors
Conclusion
References
Chapter 9
Systems Biology Applications and Bioinformatics
Abstract
Introduction
System Biology
Medicine
Agriculture
Bioremediation
Current Techniques Involved Systems Biology Application and Bioinformatics
Conclusion
References
Chapter 10
Genome Data Resources and Tools for Sequence Analysis
Abstract
Introduction to Bioinformatics
Role of Genomics
Tools for Genomics Research
FastQC
GeneWise
NCBI Prokaryotic Genomes Automatic Annotation Pipeline
GenSAS
Ori-Finder
P2RP
KAAS
Simple Synteny
MEGA11
DNA Plotter
SNP
SNP2CAPS
TASSEL
STRUCTURE
ClustalW
Bioinformatics Databases
GenBank
Phytozome
EMBL
Swiss-Prot
UniProtKB
Gramene
GrainGenes
MaizeGDB
Multiple Databases and Tools as Sources
NCBI
KEGG
Conclusion
References
Chapter 11
Bioinformatics Tools for Biomarker Discovery
Abstract
Introduction
Typical Examples of Bioinformatics Tools That Could Be Applied in the Discovery of Biomarkers
Conclusion
References
Chapter 12
A Review on Recent Advances in Different Modelling Techniques, Algorithms, and Software for Metabolic Pathways Analysis in System Biology
Abstract
Introduction
Molecular Modeling in System Biology
Concerns about Modeling and Simulation
Classification of Models
Structured Dynamical Systems
Pathway Analysis in Biological Systems
Interactions Flow through Biological Pathway
Feedback Control
Pathway Activation Measurement
Future Aspects and Challenges
Conclusion
References
Chapter 13
Bioinformatics Tools and Databases for Genomics Research
Abstract
Introduction
Relevance of Bioinformatics Tools and Databases for Genomics Research
Databases for Genomics Research
Conclusion
References
Chapter 14
Computer Viruses and Their Defences in Computer Networks: An e-Epidemiological Model
Abstract
Introduction
Simple SIVR Model
SIVR Fuzzy Model
Solution of Equilibrium Points
Basic Reproduction Number R0
Comparison R0 Versus
Local Stability for Virus Free Equilibrium
Global Stability Analysis
Control Strategies for Virus
Result and Discussion
Example 1
Conclusion
References
Glossary
Index
About the Editors
About the Contributors
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