Multimodal Data Fusion for Bioinformatics Artificial Intelligence

Name: Multimodal Data Fusion for Bioinformatics Artificial Intelligence
Brand: Wiley
Price: 188.99 EUR
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Umesh Kumar Lilhore Abhishek Kumar Narayan Vyas Sarita Simaiya Vishal Dutt(Herausgeber*in)

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Erschienen am 13. Januar 2025

406 Seiten

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Umesh Kumar Lilhore, PhD, is a postdoctoral research fellow at the University of Louisiana Lafayette, United States with more than 19 years of teaching experience and eight years of research experience. He has published many articles in reputed, peer-reviewed national and international Scopus journals and conferences. Additionally, he has served as a keynote speaker and resource person for several workshops and webinars conducted in India.

Abhishek Kumar, PhD, is an assistant director and associate professor in the Computer Science and Engineering Department at Chandigarh University, Punjab, India with more than 11 years of teaching experience. He has over 100 publications in reputed, peer-reviewed national and international journals, books and conferences and has authored/coauthored six books and edited 25 books published internationally. He has been a session chair and keynote speaker at many international conferences and webinars in India and abroad and is a member of various national and international professional societies in the field of engineering and research.

Narayan Vyas is a Technical Trainer for Research at Chandigarh University, India where he is actively involved in research and development in computer science and engineering. He has published many articles in reputed, peer-reviewed national and international Scopus journals and conferences. Additionally, he has served as a keynote speaker and resource person for several workshops and webinars conducted in India. He recently presented one article at the 2023 7th International Conference on Computing Methodologies and Communication and two articles at the 2023 International Conference on Artificial Intelligence and Smart Communication.

Sarita Simaiya, PhD, is an associate professor at the Apex Institute of Technology, Department of Computer Science and Engineering, Chandigarh University, India. She has over 15 years of academic teaching experience and has published over 80 papers, presentations, and book chapters. Her research includes digital transformation technologies such as Cloud Computing, Health care, Artificial Intelligence (AI), Quantum Computing, Internet of Things (IoT), and Modal Learning.

Vishal Dutt is an accomplished principal research consultant at AVN Innovations with extensive experience in academia and industry. He is a renowned freelance trainer for Android and Google Cloud with over seven years of academic teaching experience. He has authored over 50 publications in well-known and peer-reviewed national and international journals, SCI and Scopus journals, conferences, and book chapters. He has contributed to the editorial process of two books and is currently working on three more. Vishal has been a keynote speaker and a valuable resource for many workshops and webinars across India.

Inhalt

Cover
Series Page
Title Page
Copyright Page
Contents
Preface
Chapter 1 Advancements and Challenges in Multimodal Data Fusion for Bioinformatics AI
1.1 Introduction
1.2 Literature Review
1.3 Results and Discussion
Conclusion
References
Chapter 2 Automated Machine Learning in Bioinformatics
2.1 Introduction
2.2 Need of Automated Machine Learning
2.3 Automated ML in Various Areas of Bioinformatics
2.4 Major Obstacles for Automated ML in Various Areas of Bioinformatics
2.5 Applications of Automated ML in Various Areas of Bioinformatics
2.6 Case Study 1
2.7 Conclusion and Future Directions
References
Chapter 3 Data-Driven Discoveries: Unveiling Insights with Automated Methods
3.1 Introduction
3.2 Important Functions in Bioinformatics Include Data Mining and Analysis
3.3 Deep Learning in Bioinformatics
3.4 Challenges and Issues
3.4.1 Data Requirements for Big Data Sets
3.4.2 Model Selection and Learning Strategy
3.5 Conclusion
References
Chapter 4 Comparative Analysis of Conventional Machine Learning and Deep Learning Techniques for Predicting Parkinson's Disease
4.1 Introduction
4.2 Symptoms and Dataset for PD
4.3 Parkinson's Disease Classification Using Machine Learning Methods
4.4 Parkinson's Disease Classification Using DL Methods
4.5 Conclusion
References
Chapter 5 Foundations of Multimodal Data Fusion
Introduction
What is Multimodal Data Fusion in Bioinformatics AI?
Types of Data Modalities in Bioinformatics
Challenges and Considerations in Multimodal Data Fusion
Foundational Principles of Data Fusion
Machine Learning and Deep Learning Techniques for Multimodal Data Fusion
Feature Representation and Fusion
Applications in Bioinformatics AI
Evaluation Metrics and Validation Strategies
Evaluation Metrics
Approval Techniques
Ethical and Legal Considerations
Future Directions and Challenges
Conclusion
References
Chapter 6 Integrating IoT, Blockchain, and Quantum Machine Learning: Advancing Multimodal Data Fusion in Healthcare AI
6.1 Introduction
6.2 Internet of Things (IoT) in Healthcare
6.3 Blockchain Technology in Healthcare
6.4 Quantum Machine Learning in Healthcare
6.5 Integration of IoT, Blockchain, and Quantum Machine Learning in Healthcare
6.6 Ethical and Regulatory Considerations in Healthcare Technology
6.7 Challenges and Future Directions in Healthcare Technology Integration
6.8 Results and Discussion
6.9 Conclusion
References
Chapter 7 Integrating Multimodal Data Fusion for Advanced Biomedical Analysis: A Comprehensive Review
7.1 Introduction
7.2 Multimodal Biomedical Analysis
7.3 Challenges in Data Fusion
7.4 Deep Learning Methods for Data Fusion
7.5 Case Studies and Applications
7.5.1 Neuro-Imaging and Genetic Data Fusion
7.5.2 Multi-Omics Data Fusion for Cancer Classification
7.5.3 Clinical and Wearable Sensor Data Fusion
7.6 Future Directions
7.7 Conclusion
References
Chapter 8 Machine Learning Approaches for Integrating Imaging and Molecular Data in Bioinformatics
8.1 Introduction
8.2 Background and Motivation
8.3 Machine Learning Basics
8.4 Approaches for Data Integration
8.5 Machine Learning Techniques for Imaging and Molecular Data
8.6 Applications
8.7 Challenges and Future Directions
8.8 Case Studies
8.9 Conclusion
References
Chapter 9 Time Series Analysis in Functional Genomics
9.1 Introduction
9.2 Foundations of Time Series Analysis in Functional Genomics
9.2.1 Definition and Concept
9.2.1.1 Time Series Data in Genomics
9.2.1.2 Key Terminology
9.2.2 Challenges in Analyzing Functional Genomic Time Series Data
9.2.2.1 Noise and Variability
9.2.2.2 Data Preprocessing Considerations
9.3 Methodologies for Time Series Analysis
9.3.1 Overview of Existing Approaches
9.3.1.1 Classical Methods
9.3.1.2 Advanced Computational Techniques
9.3.2 Case Studies
9.3.2.1 Successful Applications
9.4 Applications of Time Series Analysis in Functional Genomics
9.4.1 Gene Expression Profiling
9.4.1.1 Identification of Temporal Patterns
9.4.1.2 Regulatory Network Inference
9.4.2 Functional Annotation
9.4.2.1 Enrichment Analysis
9.4.2.2 Pathway Analysis
9.4.3 Comparative Analysis
9.4.3.1 Contrasting Time Series Data Across Genomic Entities
9.5 Integration with Multimodal Data
9.5.1 Overview of Multimodal Data Fusion
9.5.2 Challenges and Opportunities in Integrating Time Series Data
9.5.2.1 Challenges in Integrating Time Series Data
9.5.2.2 Opportunities in Integrating Time Series Data
9.5.3 Case Studies on Successful Integration
9.5.3.1 Unveiling Temporal Interactions Across Multiple Modalities
9.5.3.2 Temporal Biomarkers in Disease Progression
9.6 Conclusion
References
Chapter 10 Review of Multimodal Data Fusion in Machine Learning: Methods, Challenges, Opportunities
10.1 Introduction
10.2 Related Work
10.2.1 Machine and Deep Learning Methods with Multimodal
10.2.2 Evaluation of Multimodal
10.3 Multimodal and Data Fusion
10.4 Applications, Opportunities, and Challenges
10.4.1 Audio-Visual Multimodality
10.4.2 Human-Machine Interaction (HML)
10.4.3 Understanding Brain Functionality
10.4.4 Medical Diagnosis
10.4.5 Smart Patient Monitoring
10.4.6 Remote Sensing and Earth Observations
10.4.7 Meteorological Monitoring
10.5 Conclusion and Future Directions
10.5.1 Conclusion
10.5.2 Future Directions
References
Chapter 11 Recent Advancement in Bioinformatics: An In-Depth Analysis of AI Techniques
11.1 Introduction
11.2 AutoMLDL Methods
11.3 Application of AutoMLDL in Bioinformatics
11.3.1 Bioinformatics and the Categorization of Cardiovascular Diseases
11.3.2 Diagnostics of Coronavirus Disease and Bioinformatics
11.3.3 Genomic and Bioinformatic Correlation with Clinical Data and Progress of Disease
11.3.4 Bioinformatics in the Study of Drug Resistance
11.4 Advanced Algorithm in AutoMLDL for Bioinformatics
11.4.1 Optimization with Hybrid Harris Hawks along with Cuckoo Search Applying Chemo Bioinformatics
11.4.2 The Integration of Chemoinformatics and Bioinformatics with AI
11.5 Security and Privacy Issues in AutoMLDL
11.5.1 Security and Privacy
11.5.2 Open Issues
11.6 Conclusion and Future Works
References
Chapter 12 Future Directions and Emerging Trends in Multimodal Data Fusion for Bioinformatics
12.1 Introduction
12.2 Foundational Concepts
12.3 Current State of Multimodal Data Fusion in Bioinformatics
12.4 Emerging Trends in Data Fusion
12.5 Algorithms
12.5.1 Deep Learning Architectures for Data Fusion
12.5.2 Ensemble Methods for Heterogeneous Data Integration
12.5.3 Dimensionality Reduction and Feature Extraction
12.5.4 Multi-View Learning Algorithms
12.5.5 Federated Learning for Privacy-Preserving Data Fusion
12.6 Future Directions
12.7 Case Studies and Applications
12.8 Challenges and Opportunities
12.9 Conclusion
References
Chapter 13 Future Trends in Bioinformatics AI Integration
Introduction
What Is Multimodal Data Fusion?
Types of Multimodal Data in Bioinformatics
Challenges in Multimodal Data Fusion
Multimodal Data Integration Approaches
Feature Representation and Selection
Integration of Omics Data
Clinical Applications
Imaging Data Fusion
Biological Network Integration
Applications in Precision Medicine
Computational Tools and Resources
Future Directions and Challenges
Conclusion
References
Chapter 14 Emerging Technologies in IoM: AI, Blockchain and Beyond
14.1 Introduction
14.1.1 Importance of the Internet of Medicine
14.2 Artificial Intelligence (AI) in Healthcare
14.2.1 Diagnostic Imaging and Radiology
14.2.2 Predictive Analytics and Personalized Medicine
14.2.3 Natural Language Processing (NLP) for Clinical Documentation
14.2.4 Virtual Health Assistants and Chatbots
14.2.5 Drug Discovery and Development
14.2.6 Operational Efficiency and Resource Management
14.2.7 Remote Patient Monitoring
14.2.8 Fraud Detection and Security
14.2.9 Ethical Considerations and Bias Mitigation
14.2.10 Regulatory Compliance
14.3 Blockchain in the Medical Landscape
14.3.1 Data Security and Integrity
14.3.2 Interoperability
14.3.3 Patient Empowerment
14.3.4 Supply Chain Management
14.3.5 Clinical Trials and Research
14.3.6 Smart Contracts
14.3.7 Identity Management
14.3.8 Credentialing and Certification
14.3.9 Data Sharing and Consent
14.3.10 Cybersecurity
14.4 Benefits of Using Technologies in IoM
14.4.1 Remote Monitoring and Telemedicine
14.4.2 Improved Diagnostics and Treatment
14.4.3 Genomic Medicine and Data Analytics
14.4.4 Automation and Robotics
14.4.5 Wearables and IoT Devices
14.4.6 Virtual Reality (VR) and Augmented Reality (AR)
14.4.7 Telehealth and Mobile Health (mHealth)
14.4.8 Blockchain for Healthcare Management
14.4.9 Data Analytics and AI in Research
14.4.10 Blockchain and Encryption
14.5 Integration of Cutting-Edge Technologies
14.6 Beyond AI and Blockchain: Exploring Additional Technologies
14.6.1 Internet of Things (IoT)
14.6.2 3D Printing in Medicine
14.6.3 Augmented Reality (AR) and Virtual Reality (VR)
14.6.4 Genomic Medicine
14.6.5 Robotics in Surgery
14.6.6 Natural Language Processing (NLP) and Voice Assistants
14.6.7 Edge Computing
14.7 Ethical Considerations in Implementing Emerging Technologies
14.7.1 Privacy Concerns
14.7.2 Data Security
14.7.3 Bias and Fairness
14.7.4 Transparency and Explainability
14.7.5 Informed Consent
14.7.6 Accessibility
14.7.7 Job Displacement and Employment Impact
14.7.8 Environmental Impact
14.7.9 Dual-Use Concerns
14.7.10 Accountability and Liability
14.7.11 Regulatory Compliance
14.7.12 Cultural Sensitivity
14.7.13 Long-Term Impacts
14.8 Conclusion
References
Chapter 15 Natural Language Processing in Biomedical Literature
15.1 Introduction
15.2 History
15.3 Theoretical Foundation: Natural Language Processing in Scientific Writing
15.3.1 Natural Language Processing (NLP) Overview
15.3.2 Key Concepts in NLP
15.3.3 Biomedical Text Analysis Using Natural Language Processing (NLP)
15.3.3.1 Beginning Point: Unprocessed Biomedical Text Information
15.3.3.2 Data Preprocessing
15.3.3.3 Feature Extraction
15.3.3.4 Interpretation and Analysis
15.3.3.5 Specific Tasks for Applications
15.3.3.6 Final Product: Knowledge and Insights from Biomedical Text Data
15.4 Sources of Diversity in Biomedical Literature's Natural Language Processing
15.4.1 Complexity of Linguistics
15.4.2 Heterogeneity of Data
15.4.3 Variability in Annotation
15.4.4 Domain Specificity
15.5 Disagreement and Conflict
15.6 Natural Language Processing Trends and Patterns in Biomedical Literature
15.6.1 Graphs of Biomedical Knowledge
15.6.2 In-Depth Learning Frameworks
15.6.3 Multimodal Analysis of Biomedical Data
15.6.4 Regulatory and Ethical Aspects
15.6.5 Results
15.7 Natural Language Processing's Useful Applications in Biomedical Literature
15.7.1 Annotated Text in Biomedicine
15.7.2 Systems for Clinical Decision Support (CDSS)
15.7.3 Investigating Biomedical Information
15.7.4 Biomedical Text Synopsis
15.7.5 Information Retrieval and Semantic Search
15.7.6 Evaluation of Quality and Bias Identification
15.8 Future Prospects of NLP in Biomedical Literature
15.9 Conclusion
References
Chapter 16 Biomedical Research Enrichment Through Sentiment Analysis in Patient Feedback: A Natural Language Processing Approach
16.1 Introduction
16.2 Applications of NLP
16.2.1 Machine Translation
16.2.2 Named Entity Recognition (NER)
16.2.3 Chatbots and Virtual Assistants
16.2.4 Text Classification
16.2.5 Language Translation
16.2.6 Speech Recognition
16.2.7 Text Summarization
16.2.8 Sentiment Analysis
16.3 Background Studies in Sentimental Analysis
16.4 Processes Needed for Sentimental Analysis
16.4.1 Tokenization
16.4.2 Part-of-Speech (POS) Tagging
16.4.3 Stemming and Lemmatization
16.4.4 Parsing
16.4.5 Stop Word Removal
16.4.6 Rule-Based Approaches
16.4.7 Coreference Resolution
16.4.8 Sentiment Lexicons
16.4.9 Text Pre-Processing
16.4.10 Information Extraction
16.4.11 Sentiment Analysis APIs
16.4.12 Sentiment Scoring
16.5 Conclusion
Acknowledgment
References
About the Editors
Index
Also of Interest
EULA

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