{"product_id":"multimodal-data-fusion-for-bioinformatics-artificial-intelligence-hardback-9781394269938","title":"Multimodal Data Fusion for Bioinformatics Artificial Intelligence (Hardback) 9781394269938","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eMultimodal Data Fusion for Bioinformatics Artificial Intelligence\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eUmesh Kumar Lilhore (Edited by), Lilhore (Author), Abhishek Kumar (Edited by), Narayan Vyas (Edited by), Sarita Simaiya (Edited by), Vishal Dutt (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394269938, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 28 January 2025\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e416 pages\u003cbr\u003e22.9 x 15.2 x 2.6 cm, 0.68 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003ci\u003e\u003cb\u003eMultimodal Data Fusion for Bioinformatics Artificial Intelligence\u003c\/b\u003e\u003c\/i\u003e \u003cb\u003eis 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.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003e\u003ci\u003eMultimodal Data Fusion for Bioinformatics Artificial Intelligence\u003c\/i\u003e 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. \u003c\/p\u003e\n\u003cp\u003eThe 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. \u003c\/p\u003e\n\u003cp\u003eThe 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.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Advancements and Challenges in Multimodal Data Fusion for Bioinformatics AI 1\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePriya Batta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 Literature Review 4\u003c\/p\u003e \u003cp\u003e1.3 Results and Discussion 8\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Automated Machine Learning in Bioinformatics 13\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePushpendra Kumar, Gagan Thakral, Vivek Kumar and Upendra Mishra\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 14\u003c\/p\u003e \u003cp\u003e2.2 Need of Automated Machine Learning 16\u003c\/p\u003e \u003cp\u003e2.3 Automated ML in Various Areas of Bioinformatics 19\u003c\/p\u003e \u003cp\u003e2.4 Major Obstacles for Automated ML in Various Areas of Bioinformatics 23\u003c\/p\u003e \u003cp\u003e2.5 Applications of Automated ML in Various Areas of Bioinformatics 24\u003c\/p\u003e \u003cp\u003e2.6 Case Study 1 26\u003c\/p\u003e \u003cp\u003e2.7 Conclusion and Future Directions 28\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Data-Driven Discoveries: Unveiling Insights with Automated Methods 33\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eRakhi Chauhan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 34\u003c\/p\u003e \u003cp\u003e3.2 Important Functions in Bioinformatics Include Data Mining and Analysis 36\u003c\/p\u003e \u003cp\u003e3.3 Deep Learning in Bioinformatics 39\u003c\/p\u003e \u003cp\u003e3.4 Challenges and Issues 42\u003c\/p\u003e \u003cp\u003e3.5 Conclusion 45\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Comparative Analysis of Conventional Machine Learning and Deep Learning Techniques for Predicting Parkinson's Disease 49\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMonika Sethi and Vidhu Baggan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 50\u003c\/p\u003e \u003cp\u003e4.2 Symptoms and Dataset for PD 52\u003c\/p\u003e \u003cp\u003e4.3 Parkinson's Disease Classification Using Machine Learning Methods 53\u003c\/p\u003e \u003cp\u003e4.4 Parkinson's Disease Classification Using DL Methods 57\u003c\/p\u003e \u003cp\u003e4.5 Conclusion 59\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Foundations of Multimodal Data Fusion 67\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSrinivas Kumar Palvadi and G. Kadiravan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 68\u003c\/p\u003e \u003cp\u003e5.2 What is Multimodal Data Fusion in Bioinformatics AI? 69\u003c\/p\u003e \u003cp\u003e5.3 Types of Data Modalities in Bioinformatics 70\u003c\/p\u003e \u003cp\u003e5.4 Challenges and Considerations in Multimodal Data Fusion 73\u003c\/p\u003e \u003cp\u003e5.5 Foundational Principles of Data Fusion 77\u003c\/p\u003e \u003cp\u003e5.6 Machine Learning and Deep Learning Techniques for Multimodal Data Fusion 80\u003c\/p\u003e \u003cp\u003e5.7 Feature Representation and Fusion 84\u003c\/p\u003e \u003cp\u003e5.8 Applications in Bioinformatics AI 88\u003c\/p\u003e \u003cp\u003e5.9 Evaluation Metrics and Validation Strategies 92\u003c\/p\u003e \u003cp\u003e5.10 Evaluation Metrics 93\u003c\/p\u003e \u003cp\u003e5.11 Approval Techniques 94\u003c\/p\u003e \u003cp\u003e5.12 Ethical and Legal Considerations 95\u003c\/p\u003e \u003cp\u003e5.13 Future Directions and Challenges 95\u003c\/p\u003e \u003cp\u003e5.14 Conclusion 96\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Integrating IoT, Blockchain, and Quantum Machine Learning: Advancing Multimodal Data Fusion in Healthcare AI 103\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDankan Gowda V., J. Rajalakshmi, Guruprakash B., Venkatesan Hariram and K. D. V. Prasad\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 104\u003c\/p\u003e \u003cp\u003e6.2 Internet of Things (IoT) in Healthcare 107\u003c\/p\u003e \u003cp\u003e6.3 Blockchain Technology in Healthcare 111\u003c\/p\u003e \u003cp\u003e6.4 Quantum Machine Learning in Healthcare 113\u003c\/p\u003e \u003cp\u003e6.5 Integration of IoT, Blockchain, and Quantum Machine Learning in Healthcare 116\u003c\/p\u003e \u003cp\u003e6.6 Ethical and Regulatory Considerations in Healthcare Technology 118\u003c\/p\u003e \u003cp\u003e6.7 Challenges and Future Directions in Healthcare Technology Integration 119\u003c\/p\u003e \u003cp\u003e6.8 Results and Discussion 121\u003c\/p\u003e \u003cp\u003e6.9 Conclusion 122\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Integrating Multimodal Data Fusion for Advanced Biomedical Analysis: A Comprehensive Review 127\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eUmesh Kumar Lilhore and Sarita Simaiya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 128\u003c\/p\u003e \u003cp\u003e7.2 Multimodal Biomedical Analysis 130\u003c\/p\u003e \u003cp\u003e7.3 Challenges in Data Fusion 132\u003c\/p\u003e \u003cp\u003e7.4 Deep Learning Methods for Data Fusion 134\u003c\/p\u003e \u003cp\u003e7.5 Case Studies and Applications 136\u003c\/p\u003e \u003cp\u003e7.6 Future Directions 139\u003c\/p\u003e \u003cp\u003e7.7 Conclusion 142\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Machine Learning Approaches for Integrating Imaging and Molecular Data in Bioinformatics 147\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMandeep Kaur, Dankan Gowda V., Priya. S., K.D.V. Prasad and Venkatesan Hariram\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 148\u003c\/p\u003e \u003cp\u003e8.2 Background and Motivation 152\u003c\/p\u003e \u003cp\u003e8.3 Machine Learning Basics 154\u003c\/p\u003e \u003cp\u003e8.4 Approaches for Data Integration 156\u003c\/p\u003e \u003cp\u003e8.5 Machine Learning Techniques for Imaging and Molecular Data 167\u003c\/p\u003e \u003cp\u003e8.6 Applications 168\u003c\/p\u003e \u003cp\u003e8.7 Challenges and Future Directions 170\u003c\/p\u003e \u003cp\u003e8.8 Case Studies 172\u003c\/p\u003e \u003cp\u003e8.9 Conclusion 174\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Time Series Analysis in Functional Genomics 179\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eYash Mahajan, Inderjeet Singh, Muskan Sharma and Shweta Sharma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 180\u003c\/p\u003e \u003cp\u003e9.2 Foundations of Time Series Analysis in Functional Genomics 182\u003c\/p\u003e \u003cp\u003e9.3 Methodologies for Time Series Analysis 186\u003c\/p\u003e \u003cp\u003e9.4 Applications of Time Series Analysis in Functional Genomics 194\u003c\/p\u003e \u003cp\u003e9.5 Integration with Multimodal Data 196\u003c\/p\u003e \u003cp\u003e9.6 Conclusion 199\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Review of Multimodal Data Fusion in Machine Learning: Methods, Challenges, Opportunities 205\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eLeena Arya, Yogesh Kumar Sharma, Smitha and Sreelakshmi Doma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 206\u003c\/p\u003e \u003cp\u003e10.2 Related Work 208\u003c\/p\u003e \u003cp\u003e10.3 Multimodal and Data Fusion 211\u003c\/p\u003e \u003cp\u003e10.4 Applications, Opportunities, and Challenges 216\u003c\/p\u003e \u003cp\u003e10.5 Conclusion and Future Directions 219\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Recent Advancement in Bioinformatics: An In-Depth Analysis of AI Techniques 227\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eYogesh Kumar Sharma, Leena Arya, Smitha and Shaik Saddam Hussain\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 228\u003c\/p\u003e \u003cp\u003e11.2 AutoMLDL Methods 230\u003c\/p\u003e \u003cp\u003e11.3 Application of AutoMLDL in Bioinformatics 233\u003c\/p\u003e \u003cp\u003e11.4 Advanced Algorithm in AutoMLDL for Bioinformatics 238\u003c\/p\u003e \u003cp\u003e11.5 Security and Privacy Issues in AutoMLDL 240\u003c\/p\u003e \u003cp\u003e11.6 Conclusion and Future Works 241\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Future Directions and Emerging Trends in Multimodal Data Fusion for Bioinformatics 247\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDankan Gowda V., D. Palanikkumar, K.D.V. Prasad, Mandeep Kaur and Shivoham Singh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 248\u003c\/p\u003e \u003cp\u003e12.2 Foundational Concepts 253\u003c\/p\u003e \u003cp\u003e12.3 Current State of Multimodal Data Fusion in Bioinformatics 258\u003c\/p\u003e \u003cp\u003e12.4 Emerging Trends in Data Fusion 260\u003c\/p\u003e \u003cp\u003e12.5 Algorithms 266\u003c\/p\u003e \u003cp\u003e12.6 Future Directions 272\u003c\/p\u003e \u003cp\u003e12.7 Case Studies and Applications 274\u003c\/p\u003e \u003cp\u003e12.8 Challenges and Opportunities 276\u003c\/p\u003e \u003cp\u003e12.9 Conclusion 278\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Future Trends in Bioinformatics AI Integration 283\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSrinivas Kumar Palvadi and G. Kadiravan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 284\u003c\/p\u003e \u003cp\u003e13.2 What Is Multimodal Data Fusion? 285\u003c\/p\u003e \u003cp\u003e13.3 Types of Multimodal Data in Bioinformatics 286\u003c\/p\u003e \u003cp\u003e13.4 Challenges in Multimodal Data Fusion 288\u003c\/p\u003e \u003cp\u003e13.5 Multimodal Data Integration Approaches 288\u003c\/p\u003e \u003cp\u003e13.6 Feature Representation and Selection 289\u003c\/p\u003e \u003cp\u003e13.7 Integration of Omics Data 290\u003c\/p\u003e \u003cp\u003e13.8 Clinical Applications 291\u003c\/p\u003e \u003cp\u003e13.9 Imaging Data Fusion 292\u003c\/p\u003e \u003cp\u003e13.10 Biological Network Integration 294\u003c\/p\u003e \u003cp\u003e13.11 Applications in Precision Medicine 295\u003c\/p\u003e \u003cp\u003e13.12 Computational Tools and Resources 297\u003c\/p\u003e \u003cp\u003e13.13 Future Directions and Challenges 298\u003c\/p\u003e \u003cp\u003e13.14 Conclusion 300\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Emerging Technologies in IoM: AI, Blockchain and Beyond 305\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSumit Bansal and Vandana Sindhi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 306\u003c\/p\u003e \u003cp\u003e14.2 Artificial Intelligence (AI) in Healthcare 307\u003c\/p\u003e \u003cp\u003e14.3 Blockchain in the Medical Landscape 309\u003c\/p\u003e \u003cp\u003e14.4 Benefits of Using Technologies in IoM 311\u003c\/p\u003e \u003cp\u003e14.5 Integration of Cutting-Edge Technologies 314\u003c\/p\u003e \u003cp\u003e14.6 Beyond AI and Blockchain: Exploring Additional Technologies 315\u003c\/p\u003e \u003cp\u003e14.7 Ethical Considerations in Implementing Emerging Technologies 317\u003c\/p\u003e \u003cp\u003e14.8 Conclusion 319\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Natural Language Processing in Biomedical Literature 323\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMolina Mukherjee, Prachi Punia, Adil Husain Rather and Hardik Dhiman\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 324\u003c\/p\u003e \u003cp\u003e15.2 History 326\u003c\/p\u003e \u003cp\u003e15.3 Theoretical Foundation: Natural Language Processing in Scientific Writing 327\u003c\/p\u003e \u003cp\u003e15.4 Sources of Diversity in Biomedical Literature's Natural Language Processing 330\u003c\/p\u003e \u003cp\u003e15.5 Disagreement and Conflict 332\u003c\/p\u003e \u003cp\u003e15.6 Natural Language Processing Trends and Patterns in Biomedical Literature 332\u003c\/p\u003e \u003cp\u003e15.7 Natural Language Processing's Useful Applications in Biomedical Literature 334\u003c\/p\u003e \u003cp\u003e15.8 Future Prospects of NLP in Biomedical Literature 336\u003c\/p\u003e \u003cp\u003e15.9 Conclusion 337\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Biomedical Research Enrichment Through Sentiment Analysis in Patient Feedback: A Natural Language Processing Approach 341\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSoumitra Saha, Umesh Kumar Lilhore and Sarita Simaiya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 342\u003c\/p\u003e \u003cp\u003e16.2 Applications of NLP 346\u003c\/p\u003e \u003cp\u003e16.3 Background Studies in Sentimental Analysis 353\u003c\/p\u003e \u003cp\u003e16.4 Processes Needed for Sentimental Analysis 359\u003c\/p\u003e \u003cp\u003e16.5 Conclusion 369\u003c\/p\u003e \u003cp\u003eAcknowledgment 370\u003c\/p\u003e \u003cp\u003eReferences 370\u003c\/p\u003e \u003cp\u003eAbout the Editors 375\u003c\/p\u003e \u003cp\u003eIndex 377\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Biology, life sciences [\u003ca title=\"See our other books on Biology, life sciences\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Biology,%20life%20sciences%20%5BPS%5D%22\"\u003ePS\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52433242620184,"sku":"9781394269938","price":146.36,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394269938.jpg?v=1784852899","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/multimodal-data-fusion-for-bioinformatics-artificial-intelligence-hardback-9781394269938","provider":"Freshly Printed Books","version":"1.0","type":"link"}