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Intelligent Data Analytics for Bioinformatics and Biomedical Systems

Edited by Neha Sharma, Korhan Cengiz and Prasenjit Chatterjee
Series: Sustainable Computing and Optimization
Copyright: 2024   |   Status: Published
ISBN: 9781394270880  |  Hardcover  |  
428 pages
Price: $225 USD
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One Line Description
The book analyzes the combination of intelligent data analytics with the intricacies of biological data that has become a crucial factor for innovation and growth
in the fast-changing field of bioinformatics and biomedical systems.

Audience
Intelligent Data Analytics for Bioinformatics and Biomedical Systems is primarily targeted to professionals and researchers in bioinformatics, genetics, molecular biology, biomedical engineering, and healthcare. The book will also suit academicians, students, and professionals working in pharmaceuticals and interpreting biomedical data.

Description
Intelligent Data Analytics for Bioinformatics and Biomedical Systems delves into the transformative nature of data analytics for bioinformatics and biomedical research. It offers a thorough examination of advanced techniques, methodologies, and applications that utilize intelligence to improve results in the healthcare sector. With the exponential growth of data in these domains, the book explores how computational intelligence and advanced analytic techniques can be harnessed to extract insights, drive informed decisions, and unlock hidden patterns from vast datasets. From genomic analysis to disease diagnostics and personalized medicine, the book aims to showcase intelligent approaches that enable researchers, clinicians, and data scientists to unravel complex biological processes and make significant strides in understanding human health and diseases.
This book is divided into three sections, each focusing on computational intelligence and data sets in biomedical systems. The first section discusses the fundamental concepts of computational intelligence and big data in the context of bioinformatics. This section emphasizes data mining, pattern recognition, and knowledge discovery for bioinformatics applications. The second part talks about computational intelligence and big data in biomedical systems. Based on how these advanced techniques are utilized in the system, this section discusses how personalized medicine and precision healthcare enable treatment based on individual data and genetic profiles. The last section investigates the challenges and future directions of computational intelligence and big data in bioinformatics and biomedical systems. This section concludes with discussions on the potential impact of computational intelligence on addressing global healthcare challenges.

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Author / Editor Details
Neha Sharma PhD, is an assistant professor in the Department of Computer Science and Engineering, Chitkara University, Rajpura, India. She has more than 60 international publications in reputed peer-reviewed journals. She has also published more than 30 national & international patents under the Intellectual Property Rights of the governments of India and abroad. Her main areas of research are in image processing, machine learning, deep learning, and cybersecurity.

Korhan Cengiz, PhD, is an assistant professor at the Department of Information Technologies, Faculty of Informatics and Management, University of Hradec Kralove, Kralove, Czech Republic. He obtained his doctorate in electronics engineering from Kadir Has University, Istanbul, Turkey, in 2016 and has authored more than 40 SCI articles, five international patents, ten chapters in books, and one book. His research interests include wireless sensor networks, wireless communications, statistical signal processing, etc.

Prasenjit Chatterjee, PhD, is a professor of mechanical engineering and dean (research and consultancy) at MCKV Institute of Engineering, West Bengal, India. He has authored several books on intelligent decision-making, fuzzy computing, supply chain management, etc. He has over 6850 citations and many research papers in various international journals. Dr. Chatterjee is one of the developers of two multiple-criteria decision-making methods called Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) and Ranking Alternatives through Functional Mapping of Criterion Sub-Intervals into a Single Interval (RAFSI).

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Table of Contents
Preface
Acknowledgment
1. Advancements in Machine Learning Techniques for Biological Data Analysis
S. Kanakaprabha, G. Ganesh Kumar, Y. Padma, Gangavarapu and Venkata Nagaraju Thatha
1.1 Introduction
1.1.1 Significance of Advanced Data Analysis in Biology
1.2 Literature Survey
1.3 Machine Learning Fundamentals
1.3.1 Supervised, Unsupervised, and Semi-Supervised Learning
1.3.2 Feature Engineering and Selection
1.3.3 Deep Learning Architectures for Biological Data
1.4 Genomic Sequence Analysis
1.4.1 DNA Sequence Classification and Prediction
1.4.2 Genomic Variant Analysis with Machine Learning
1.4.3 Enhancing Epigenetic Studies through AI
1.5 Proteomic Profiling and Structural Prediction
1.5.1 Protein Structure Prediction Using Deep Learning
1.5.2 Peptide and Protein Identification via Machine Learning
1.5.3 Functional Annotation of Proteins
1.6 Metabolomics and Pathway Analysis
1.6.1 Metabolite Identification and Quantification
1.6.2 Metabolic Pathway Reconstruction Using AI
1.6.3 Integrative Analysis of Multi-Omics Data
1.7 Medical Applications
1.7.1 Disease Diagnosis and Biomarker Discovery
1.7.2 Personalized Treatment and Drug Discovery
1.7.3 Predictive Modeling for Clinical Outcomes
1.7.4 Drug Repurposing and Adverse Event Prediction
1.7.5 Neuroinformatics and Brain Disorders
1.8 Challenges and Future Directions
1.8.1 Interpretable Machine Learning in Biology
1.8.2 Addressing Data Privacy and Ethics
1.8.3 Advancing Quantum Computing in Biological Data Analysis
1.8.4 Handling Heterogeneous and Multi-Modal Data
1.8.5 Small Data and Imbalanced Datasets
1.8.6 Clinical Adoption and Validation
1.8.7 Ethical and Societal Implications
1.9 Conclusion
1.9.1 Synthesis of Key Contributions and Insights
1.9.2 Anticipated Transformations in Biological Research
References
2. Predictive Analytics in Medical Diagnosis
Vivek Upadhyaya
2.1 Introduction to Predictive Analytics in Healthcare
2.1.1 Definition of Predictive Analytics
2.1.2 The Significance of Predictive Analytics in Medical Diagnosis
2.2 Overview of the Chapter’s Structure
2.3 Data Sources and Data Preprocessing
2.3.1 Types of Data Sources (Electronic Health Records, Wearable Devices, Genetic Data, etc.)
2.4 Data Quality and Cleaning
2.4.1 Feature Selection and Engineering
2.4.2 Dealing with Missing Data
2.5 Predictive Analytics Techniques
2.5.1 Regression Analysis
2.5.2 Classification Models (e.g., Logistic Regression, Decision Trees, Random Forests)
2.5.3 Machine Learning Algorithms (e.g., Support Vector Machines, Neural Networks)
2.5.4 Time Series Analysis
2.6 Use Cases in Medical Diagnosis
2.6.1 Early Detection of Diseases (e.g., Cancer, Diabetes)
2.6.2 Risk Assessment and Stratification
2.6.3 Personalized Treatment Recommendations
2.6.4 Image Analysis and Medical Imaging
2.6.5 Disease Progression Tracking
2.6.6 Model Interpretability and Explainability
2.6.7 The Importance of Model Interpretability in Healthcare
2.6.8 Techniques for Making Predictive Models More Interpretable
2.6.9 Regulatory Considerations (e.g., GDPR, HIPAA)
2.6.10 Ethical and Legal Considerations
2.7 Challenges and Limitations
2.7.1 Data-Related Challenges (Data Volume, Quality, Interoperability)
2.7.2 Overfitting and Model Generalization
2.7.3 Addressing Bias and Fairness in Predictive Models
2.7.4 Successful Implementation and Case Studies
2.7.5 Real-World Examples of Healthcare Institutions Successfully Using Predictive Analytics
2.8 Future Trends and Innovations
2.8.1 The Role of Artificial Intelligence and Deep Learning
2.8.2 Integration with Electronic Health Records and Telemedicine
2.8.3 The Potential Impact of Quantum Computing on Medical Diagnosis
2.9 Conclusion
References
3. Skin Disease Detection and Classification
M. Aamir Gulzar, Salman Iqbal, Akhtar Jamil, Alaa Ali Hameed and Faezeh Soleimani
3.1 Introduction
3.2 Related Work
3.3 Data
3.4 Methodology
3.4.1 Data Pre-Processing
3.4.2 Image Enhancement
3.4.3 Feature Extraction
3.4.4 Machine Learning Algorithm Used
3.5 Results
3.5.1 Experimental Setup
3.5.2 Data Preprocessing, Feature Extraction, and Model Selection
3.5.3 Evaluation Metrics
3.5.4 Classification and Outcomes
3.6 Conclusion
3.7 Future Work
References
4. Computer-Aided Polyp Detection Using Customized Convolutional Neural Network Architecture
Palak Handa, Nidhi Goel, S. Indu and Deepak Gunjan
4.1 Introduction
4.2 Related Works
4.3 Materials and Methods
4.3.1 Description of the Used Datasets and Their Preparation
4.3.2 Data Augmentation
4.3.3 Customized CNN
4.4 Results and Discussion
4.4.1 CNN Optimizers
4.4.2 Kernel Initializers
4.4.3 Color Space
4.4.4 Image Dimension
4.4.5 Kernel Size
4.4.6 Sample Maps of the CNN Features
4.4.7 Ablation Study
4.4.8 Comparison of the Proposed Architecture with Existing Deep-Learning Algorithms in This Field
4.5 Conclusion and Future Scope
References
5. Computational Intelligence Induced Risk in Modern Healthcare: Classical Review and Current Status
Nitish Ojha and Shrikant Ojha
5.1 Introduction
5.2 People-Based Risk
5.3 Doctor-Induced Risk
5.4 Patient-Based Risk
5.5 Process-Based Risk
5.6 Technology-Based Risk
5.7 Conclusion
References
6. A Hybrid Deep Learning Framework to Diagnose Sleep Apnea Using Electrocardiogram Signals for Smart Healthcare
Sampoorna Poria, Ahona Ghosh, Biswarup Ganguly and Sriparna Saha
6.1 Introduction
6.2 Proposed Methodology
6.2.1 Introduction to the Data Acquisition Device
6.2.2 Preprocessing Using Discrete Wavelet Transform
6.2.3 Feature Extraction Using Auto Encoder
6.2.4 Classification Using Bidirectional LSTM
6.3 Experiment Results and Discussions
6.3.1 Dataset Details
6.3.1.1 Preprocessing Outcomes
6.3.2 Feature Extraction Outcomes
6.3.3 Classification Results
6.3.4 Statistical Validation
6.3.5 Experimental Setup for Computer Aided Diagnosis System
6.3.6 Performance Evaluation
6.4 Conclusion and Future Scope
Acknowledgments
References
7. Deep Ensemble Feature Extraction Based Classification of Bleeding Regions Using Wireless Capsule Endoscopy Images
Srijita Bandopadhyay, Kyamelia Roy, Sheli Sinha Chaudhuri, Soumen Banerjee and Korhan Cengiz
7.1 Introduction
7.2 Related Works
7.3 Methodology
7.3.1 Dataset
7.3.2 Image Processing
7.3.3 Histogram Equalizer
7.3.4 Denoising
7.3.5 Adaptive Filtering
7.3.6 Augmentation
7.3.7 Data Processing
7.3.8 Convolutional Neural Network
7.3.8.1 ResNet 50
7.3.8.2 VGG 16
7.3.8.3 Inception V3
7.3.9 Feature Extraction
7.3.10 Feature Reconstruction
7.3.11 Classification
7.4 Results and Discussion
7.5 Conclusion
References
8. Advances in Brain Tumor Detection and Localization: A Comprehensive Survey
Krishnangshu Paul, Arunima Patra and Prithwineel Paul
8.1 Introduction
8.2 Background Study on Various Methods
8.2.1 SVM
8.2.1.1 Advantages
8.2.1.2 Limitations
8.2.2 KNN
8.2.2.1 Advantages
8.2.2.2 Limitations
8.2.3 Logistic Regression
8.2.3.1 Advantages
8.2.3.2 Limitations
8.2.4 CNN
8.2.4.1 Advantages
8.2.4.2 Limitations
8.3 Methodology
8.4 Experimentation
8.4.1 Dataset
8.4.2 Results Achieved
8.5 Discussion
8.6 Conclusion
8.6.1 Future Scope
References
9. Integrating Apriori Algorithm with Data Mining Classification Techniques for Enhanced Primary Tumor Prediction
Khalid Mahboob, Nida Khalil, Fatima Waseem and Abeer Javed Syed
9.1 Overview
9.1.1 Feature Selection
9.1.2 Hyperparameter Tuning
9.1.3 Enhanced Primary Tumor Prediction
9.1.4 Continuous Improvement
9.1.5 Clinical Integration
9.2 Previous Studies on Tumor Prediction Using Data Mining and Apriori Algorithm
9.3 Data Mining Process
9.3.1 Data Collection and Pre-Processing
9.3.1.1 Data Cleaning
9.3.1.2 Data Transformation
9.3.1.3 Data Reduction
9.3.1.4 Data Integration
9.3.1.5 Data Discretization
9.3.2 Model(s) Selection and Building
9.3.2.1 Supervised Learning
9.3.2.2 Unsupervised Learning
9.3.2.3 Reinforcement Learning
9.3.2.4 Ensemble Method
9.3.3 Evaluation and Exploratory Data Analysis
9.3.3.1 Evaluation Techniques in Data Mining
9.4 Data Mining in Bioinformatics
9.5 Cancer and Tumor Biology
9.6 Data Mining Classification Techniques
9.6.1 J48 Decision Tree
9.6.2 Naïve Bayes
9.6.3 K-Nearest Neighbor
9.7 Apriori Algorithm and Association Rule Mining
9.8 Conclusion and Future Work
References
10. Deep Learning in Genomics, Personalized Medicine, and Neurodevelopmental Disorders
Ajay Sharma, Shashi Kala, Aman Kumar, Shamneesh Sharma, Gaurav Gupta and Varun Jaiswal
10.1 Introduction
10.1.1 Genomics, Genetics, and Personalized-Medicine Genetics
10.1.2 The “Omics” Revolution a Bioinformatics Perspective
10.2 Machine Learning in Personalized Medicine and Neurogenerative Disorder
10.2.1 Machine Learning Using Artificial Deep Neural Networks (DNN)
10.2.2 Limitations and Advantages of ML Over Traditional Approaches
10.3 Machine Learning in Genomics
10.3.1 Multi-Model Data Integration Using Machine Learning
10.4 Machine Learning and the Future of Medicine in Healthcare
10.4.1 Ethical and Legal Considerations of Precision Medicine
10.5 Genomics Technology and Application
10.5.1 High-Throughput DNA Sequencing Technology
10.5.2 Pharmacogenomics (PGx)
10.5.3 The Study of Drug Action is Divided into Different Categories: Pharmacokinetics and Pharmacodynamics
10.5.4 Circulating Cell-Free Nucleic Acids
10.5.5 Circulating Tumor Cells (CTCs)
10.5.6 Mitochondrial DNA (mtDNA)
10.6 Artificial Intelligence and Neurodegenerative Disorders
10.7 Conclusion
Conflict of Interest
Acknowledgments
References
11. Emerging Trends of Big Data in Bioinformatics and Challenges
Ajay Sharma, Tarun Pal, Utkarsha Naithani, Gaurav Gupta and Varun Jaiswal
11.1 Introduction
11.2 Human Genome
11.3 Next-Generation Sequencing
11.3.1 Challenges of NGS in Big Data
11.4 Bioinformatics Big Data Architecture
11.5 Big Data in Immunology
11.6 Structural Biology
11.7 Computer Science
11.8 Healthcare
11.8.1 Application of Big Data in Healthcare
11.9 Big Data Formats
11.9.1 Quantum Computing
11.10 Conclusion
Conflict of Interest
Acknowledgments
References
12. Wearable Devices and Health Monitoring: Big Data and AI for Remote Patient Care
S. Kanakaprabha, G. Ganesh Kumar, Bhargavi Peddi Reddy, Yallapragada Ravi Raju and P. Chandra Mohan Rai
12.1 Introduction
12.1.1 Importance of Remote Patient Monitoring
12.1.2 Significance of Big Data and AI in Healthcare
12.2 Related Work
12.3 Wearable Technologies in Healthcare
12.3.1 Types of Wearable Devices (Smartwatches, Fitness Trackers, Medical-Grade Wearables, etc.)
12.3.2 Applications in Monitoring Vital Signs (Heart Rate, Blood Pressure, Temperature, etc.)
12.3.3 Wearables for Tracking Physical Activity and Sleep Patterns
12.4 Remote Patient Monitoring
12.4.1 Definition and Benefits of Remote Patient Monitoring
12.5 Use Cases: Chronic Disease Management, Post‑Operative Care, Elderly Care, Etc.
12.6 Challenges of Traditional In-Person Care vs. Remote Monitoring
12.7 Data Collection and Transmission
12.7.1 Sensors and Data Collection Methods in Wearables
12.8 Wireless Data Transmission Technologies (Bluetooth, Wi-Fi, Cellular, Etc.)
12.8.1 Ensuring Data Security and Privacy
12.8.2 Big-Data Analytics in Healthcare
12.8.3 Role of Big Data in Healthcare Decision-Making
12.8.4 Handling and Processing Large Volumes of Wearable‑Generated Data
12.8.5 Data Storage, Integration, and Interoperability
12.8.6 AI and Machine Learning in Health Monitoring
12.9 Introduction to AI and ML Applications in Healthcare
12.9.1 Predictive Analytics for Early Disease Detection
12.9.2 Real-Time Anomaly Detection and Alerts
12.9.3 Clinical Decision Support Systems
12.9.4 Integration of AI Insights into Clinical Workflows
12.9.5 Enabling Personalized Treatment Plans Based on Wearable Data
12.9.6 Enhancing Healthcare Professional Decision-Making
12.9.7 Challenges and Ethical Considerations in Using Patient‑Generated Data
12.10 Future Directions and Trends
12.11 Conclusion
References
13. Disease Biomarker Discovery with Big Data Analysis
G. Venu Gopal, Kanakaprabha S., Gangavarapu Moahana Rao, Yallapragada Ravi Raju and G. Ganesh Kumar
13.1 Introduction
13.1.1 The Need for Multi-Omics Data Integration in Biomarker Discovery
13.1.2 Role of Machine Learning in Multi-Omics Data Analysis
13.2 Literature Survey
13.3 Challenges in Multi-Omics Data Integration
13.3.1 Data Heterogeneity and Integration Challenges
13.3.2 Dimensionality Reduction and Feature Selection
13.3.3 Feature Representation and Integration Techniques
13.3.4 Early Fusion vs. Late Fusion Approaches
13.3.5 Network-Based Integration Methods
13.4 Deep Learning Architectures for Multi-Omics Data
13.4.1 Disease Subtyping and Stratification
13.4.2 Identification of Key Regulatory Pathways
13.4.3 Predictive Modeling for Treatment Response
13.4.4 Cancer Biomarker Discovery Using Multi-Omics Data
13.4.5 Neurological Disorder Classification through Integration
13.5 Evaluation Metrics and Validation Strategies
13.5.1 Cross-Validation Techniques for Multi-Omics Data
13.5.2 Assessing Robustness and Generalizability of Biomarker Models
13.6 Ethical Considerations in Biomarker Discovery
13.6.1 Privacy and Security of Patient Data
13.6.2 Bias and Fairness in Machine Learning Models
13.6.3 Integration of Single-Cell Omics Data
13.6.4 Explainable AI for Biomarker Discovery
13.6.5 Personalized Medicine and Biomarker-Based Therapies
13.7 Conclusion
References
14. Real-Time Epilepsy Monitoring and Alerting System Using IoT Devices and Machine Learning Techniques in Blockchain-Based Environment
Mohsen Ghorbian and Saeid Ghorbian
14.1 Introduction
14.2 Preliminaries
14.2.1 Overview of IoT Technology
14.2.2 Blockchain Technology
14.2.3 Overview of ML Technology
14.2.4 Epilepsy Disease
14.3 IoT and ML in Healthcare
14.3.1 HLF Architectural Framework
14.3.2 Epilepsy Detection Procedures
14.3.3 Various Approaches to ML
14.4 Incorporating ML with IoT in the Blockchain
14.5 Intelligent Alert Mechanism in IoT Healthcare
14.5.1 Data Gathering, Transmission, and Storage
14.5.2 Analyzing Stored Data
14.5.3 Sending an Alert Message
14.6 Conclusion
References
15. Integrating Quantum Computing in Bioinformatics and Biomedical Research
Prasad Selladurai, Ruby Dahiya, Baskar Kandasamy and Venkateswaran Radhakrishnan
15.1 Introduction
15.1.1 Quantum Computing
15.1.2 The Role of Quantum Computing in Bioinformatics
15.1.3 Application of Quantum Technologies
15.1.4 Characteristics of Quantum Computing in Bioinformatics
15.1.5 What are the Tools Used in Quantum Computing in Bioinformatics?
15.2 Novel Approaches of Quantum Computing in Bioinformatics
15.2.1 Quantum Chemistry for Drug Discovery
15.2.2 A Quantum Advance in Genetics
15.2.3 Hybrid Quantum-Classical Approaches
15.2.4 Quantum-Inspired Machine Learning
15.2.5 Challenges and Limitations
15.3 Conclusion
15.4 The Future of Quantum Computing in Bioinformatics and Biomedical Research
References
16. Future Perspective and Emerging Trends in Computational Intelligence
Chander Prabha
16.1 Introduction
16.2 Emerging Trends in CI for Bioinformatics
16.3 CI Emerging Trends for Biomedical Systems
16.4 CI Future Perspective in Bioinformatics
16.5 The Future of CI in Biomedical Systems
16.6 Conclusion and Future Scope
References
Index

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