Multimodal data fusion for bioinformatics artificial intelligence/ edited by Umesh Kumar Lilhore... [and 4 others].

Format:

Book

Contributor:

Lilhore, Umesh Kumar, editor.

Vyas, Narayan, editor.

Wiley InterScience (Online service)

Language:

English

Subjects (All):

Bioinformatics.

Artificial intelligence--Biological applications.

Artificial intelligence.

Physical Description:

1 online resource

Place of Publication:

Hoboken, NJ : John Wiley & Sons, Inc. ; Beverly, MA : Scrivener Publishing LLC, 2025.

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

Notes:

Electronic reproduction. Hoboken, N.J. Available via World Wide Web.

Description based on online resource; title from digital title page (viewed on March 18, 2025).

Other Format:

Print version:

ISBN:

9781394269952

1394269951

9781394269969

139426996X

9781394269945

1394269943

Publisher Number:

90102600534

Access Restriction:

Restricted for use by site license.