Multimodal Data Fusion for Bioinformatics Artificial Intelligence

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.

• Preface xv1 Advancements and Challenges in Multimodal Data Fusion for Bioinformatics AI 1Priya Batta1.1 Introduction 11.2 Literature Review 41.3 Results and Discussion 82 Automated Machine Learning in Bioinformatics 13Pushpendra Kumar, Gagan Thakral, Vivek Kumar and Upendra Mishra2.1 Introduction 142.2 Need of Automated Machine Learning 162.3 Automated ML in Various Areas of Bioinformatics 192.4 Major Obstacles for Automated ML in Various Areas of Bioinformatics 232.5 Applications of Automated ML in Various Areas of Bioinformatics 242.6 Case Study 1 262.7 Conclusion and Future Directions 283 Data-Driven Discoveries: Unveiling Insights with Automated Methods 33Rakhi Chauhan3.1 Introduction 343.2 Important Functions in Bioinformatics Include Data Mining and Analysis 363.3 Deep Learning in Bioinformatics 393.4 Challenges and Issues 423.5 Conclusion 454 Comparative Analysis of Conventional Machine Learning and Deep Learning Techniques for Predicting Parkinson's Disease 49Monika Sethi and Vidhu Baggan4.1 Introduction 504.2 Symptoms and Dataset for PD 524.3 Parkinson's Disease Classification Using Machine Learning Methods 534.4 Parkinson's Disease Classification Using DL Methods 574.5 Conclusion 595 Foundations of Multimodal Data Fusion 67Srinivas Kumar Palvadi and G. Kadiravan5.1 Introduction 685.2 What is Multimodal Data Fusion in Bioinformatics AI? 695.3 Types of Data Modalities in Bioinformatics 705.4 Challenges and Considerations in Multimodal Data Fusion 735.5 Foundational Principles of Data Fusion 775.6 Machine Learning and Deep Learning Techniques for Multimodal Data Fusion 805.7 Feature Representation and Fusion 845.8 Applications in Bioinformatics AI 885.9 Evaluation Metrics and Validation Strategies 925.10 Evaluation Metrics 935.11 Approval Techniques 945.12 Ethical and Legal Considerations 955.13 Future Directions and Challenges 955.14 Conclusion 966 Integrating IoT, Blockchain, and Quantum Machine Learning: Advancing Multimodal Data Fusion in Healthcare AI 103Dankan Gowda V., J. Rajalakshmi, Guruprakash B., Venkatesan Hariram and K. D. V. Prasad6.1 Introduction 1046.2 Internet of Things (IoT) in Healthcare 1076.3 Blockchain Technology in Healthcare 1116.4 Quantum Machine Learning in Healthcare 1136.5 Integration of IoT, Blockchain, and Quantum Machine Learning in Healthcare 1166.6 Ethical and Regulatory Considerations in Healthcare Technology 1186.7 Challenges and Future Directions in Healthcare Technology Integration 1196.8 Results and Discussion 1216.9 Conclusion 1227 Integrating Multimodal Data Fusion for Advanced Biomedical Analysis: A Comprehensive Review 127Umesh Kumar Lilhore and Sarita Simaiya7.1 Introduction 1287.2 Multimodal Biomedical Analysis 1307.3 Challenges in Data Fusion 1327.4 Deep Learning Methods for Data Fusion 1347.5 Case Studies and Applications 1367.6 Future Directions 1397.7 Conclusion 1428 Machine Learning Approaches for Integrating Imaging and Molecular Data in Bioinformatics 147Mandeep Kaur, Dankan Gowda V., Priya. S., K.D.V. Prasad and Venkatesan Hariram8.1 Introduction 1488.2 Background and Motivation 1528.3 Machine Learning Basics 1548.4 Approaches for Data Integration 1568.5 Machine Learning Techniques for Imaging and Molecular Data 1678.6 Applications 1688.7 Challenges and Future Directions 1708.8 Case Studies 1728.9 Conclusion 1749 Time Series Analysis in Functional Genomics 179Yash Mahajan, Inderjeet Singh, Muskan Sharma and Shweta Sharma9.1 Introduction 1809.2 Foundations of Time Series Analysis in Functional Genomics 1829.3 Methodologies for Time Series Analysis 1869.4 Applications of Time Series Analysis in Functional Genomics 1949.5 Integration with Multimodal Data 1969.6 Conclusion 19910 Review of Multimodal Data Fusion in Machine Learning: Methods, Challenges, Opportunities 205Leena Arya, Yogesh Kumar Sharma, Smitha and Sreelakshmi Doma10.1 Introduction 20610.2 Related Work 20810.3 Multimodal and Data Fusion 21110.4 Applications, Opportunities, and Challenges 21610.5 Conclusion and Future Directions 21911 Recent Advancement in Bioinformatics: An In-Depth Analysis of AI Techniques 227Yogesh Kumar Sharma, Leena Arya, Smitha and Shaik Saddam Hussain11.1 Introduction 22811.2 AutoMLDL Methods 23011.3 Application of AutoMLDL in Bioinformatics 23311.4 Advanced Algorithm in AutoMLDL for Bioinformatics 23811.5 Security and Privacy Issues in AutoMLDL 24011.6 Conclusion and Future Works 24112 Future Directions and Emerging Trends in Multimodal Data Fusion for Bioinformatics 247Dankan Gowda V., D. Palanikkumar, K.D.V. Prasad, Mandeep Kaur and Shivoham Singh12.1 Introduction 24812.2 Foundational Concepts 25312.3 Current State of Multimodal Data Fusion in Bioinformatics 25812.4 Emerging Trends in Data Fusion 26012.5 Algorithms 26612.6 Future Directions 27212.7 Case Studies and Applications 27412.8 Challenges and Opportunities 27612.9 Conclusion 27813 Future Trends in Bioinformatics AI Integration 283Srinivas Kumar Palvadi and G. Kadiravan13.1 Introduction 28413.2 What Is Multimodal Data Fusion? 28513.3 Types of Multimodal Data in Bioinformatics 28613.4 Challenges in Multimodal Data Fusion 28813.5 Multimodal Data Integration Approaches 28813.6 Feature Representation and Selection 28913.7 Integration of Omics Data 29013.8 Clinical Applications 29113.9 Imaging Data Fusion 29213.10 Biological Network Integration 29413.11 Applications in Precision Medicine 29513.12 Computational Tools and Resources 29713.13 Future Directions and Challenges 29813.14 Conclusion 30014 Emerging Technologies in IoM: AI, Blockchain and Beyond 305Sumit Bansal and Vandana Sindhi14.1 Introduction 30614.2 Artificial Intelligence (AI) in Healthcare 30714.3 Blockchain in the Medical Landscape 30914.4 Benefits of Using Technologies in IoM 31114.5 Integration of Cutting-Edge Technologies 31414.6 Beyond AI and Blockchain: Exploring Additional Technologies 31514.7 Ethical Considerations in Implementing Emerging Technologies 31714.8 Conclusion 31915 Natural Language Processing in Biomedical Literature 323Molina Mukherjee, Prachi Punia, Adil Husain Rather and Hardik Dhiman15.1 Introduction 32415.2 History 32615.3 Theoretical Foundation: Natural Language Processing in Scientific Writing 32715.4 Sources of Diversity in Biomedical Literature's Natural Language Processing 33015.5 Disagreement and Conflict 33215.6 Natural Language Processing Trends and Patterns in Biomedical Literature 33215.7 Natural Language Processing's Useful Applications in Biomedical Literature 33415.8 Future Prospects of NLP in Biomedical Literature 33615.9 Conclusion 33716 Biomedical Research Enrichment Through Sentiment Analysis in Patient Feedback: A Natural Language Processing Approach 341Soumitra Saha, Umesh Kumar Lilhore and Sarita Simaiya16.1 Introduction 34216.2 Applications of NLP 34616.3 Background Studies in Sentimental Analysis 35316.4 Processes Needed for Sentimental Analysis 35916.5 Conclusion 369Acknowledgment 370References 370About the Editors 375Index 377