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Multimodal data fusion for bioinformatics artificial intelligence/

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Multimodal data fusion for bioinformatics artificial intelligence/edited by Umesh Kumar Lilhore... [and 4 others].
other author:	Vyas, Narayan,
Description:	xvi,381 pages :illustrations ;24cm.
Subject:	Bioinformatics.
ISBN:	9781394269952

Multimodal data fusion for bioinformatics artificial intelligence/
Multimodal data fusion for bioinformatics artificial intelligence/edited by Umesh Kumar Lilhore... [and 4 others]. - xvi,381 pages :illustrations ;24cm.

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

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.

ISBN: 9781394269952

Standard No.: 10.1002/9781394269969doi

Source: 9781394269938O'Reilly Media

Nat. Bib. No.: GBC506823bnb

Nat. Bib. Agency Control No.: 300482771UkSubjects--Topical Terms:

194415
Bioinformatics.
Index Terms--Genre/Form:

214472
Electronic books.

LC Class. No.: QH324.2 / .M85 2025

Dewey Class. No.: 570.285

Multimodal data fusion for bioinformatics artificial intelligence/
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505 0 $a 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
505 8 $a 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
505 8 $a 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
505 8 $a 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
505 8 $a 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
520 $a 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.
588 $a Description based on online resource; title from digital title page (viewed on March 18, 2025).
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655 0 $a Electronic books. $2 local. $3 214472
700 1 $a Vyas, Narayan, $e editor. $3 1009510
700 1 $a Lilhore, Umesh Kumar, $e editor. $3 1009509
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