Multimodal Data Fusion for Bioinformatics Artificial Intelligence.

Format:

Book

Author/Creator:

Lilhore, Umesh Kumar.

Contributor:

Kumar, Abhishek.

Vyas, Narayan.

Simaiya, Sarita.

Dutt, Vishal.

Language:

English

Subjects (All):

Bioinformatics.

Artificial intelligence--Biological applications.

Physical Description:

1 online resource (406 pages)

Edition:

1st ed.

Place of Publication:

Newark : John Wiley & Sons, Incorporated, 2025.

Summary:

Multimodal Data Fusion for Bioinformatics Artificial Intelligence is a must-have for anyone interested in the intersection of AI and bioinformatics, as it delves into innovative data fusion methods and their applications in 'omics' research while addressing the ethical implications and future developments shaping the field today. Multimodal Data Fusion for Bioinformatics Artificial Intelligence is an indispensable resource for those exploring how cutting-edge data fusion methods interact with the rapidly developing field of bioinformatics. Beginning with the basics of integrating different data types, this book delves into the use of AI for processing and understanding complex "omics" data, ranging from genomics to metabolomics. The revolutionary potential of AI techniques in bioinformatics is thoroughly explored, including the use of neural networks, graph-based algorithms, single-cell RNA sequencing, and other cutting-edge topics. The second half of the book focuses on the ethical and practical implications of using AI in bioinformatics. The tangible benefits of these technologies in healthcare and research are highlighted in chapters devoted to precision medicine, drug development, and biomedical literature. The book addresses a wide range of ethical concerns, from data privacy to model interpretability, providing readers with a well-rounded education on the subject. Finally, the book explores forward-looking developments such as quantum computing and augmented reality in bioinformatics AI. This comprehensive resource offers a bird's-eye view of the intersection of AI, data fusion, and bioinformatics, catering to readers of all experience levels.

Contents:

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

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

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

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

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

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

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

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

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

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

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

Chapter 13 Future Trends in Bioinformatics AI Integration

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

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.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9781394269969

139426996X

9781394269945

1394269943

9781394269952

1394269951

OCLC:

1484697059