Narayan Vyas, an academician at Vivekananda Global University, specializes in computer science, focusing on IoT and Mobile App Development. He has trained over 1000 students globally and published extensively in Scopus journals. An active IEEE member, his research interests include Remote Sensing, Machine Learning, and Computer Vision.
Multimodal Data Fusion for Bioinformatics AI
Publisher: Wiley-Scrivener
Editors: Umesh Kumar Lilhore, Abhishek Kumar, Narayan Vyas, Sarita Simaiya, Vishal Dutt
This book is an indispensable resource for anyone interested in how cutting-edge data fusion methods and the rapidly developing area of bioinformatics interact. Starting with the basics of integrating different data types, this book goes 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 addressed, including using neural networks, graph-based algorithms, single-cell RNA sequencing, and other cutting-edge issues.
| Important Dates | |
|---|---|
| Abstract Submission Deadline | 30 December 2023 |
| Abstract Acceptance Notification | 30 January 2024 |
| Full Chapter Submission Deadline | 10 March 2024 |
| Chapter Acceptance Notification | 10 May 2024 |
| Projected Book Release Date | December 2024 |
| Important Guidelines | |
|---|---|
| Citation Style | APA (7th Edition) |
| Formatting | 11 pt Times Roman, 1.5 line spacing |
| Originality | Plagiarism Under 10%, 0% AI Generated Content |
| Headings |
|
Chapter 1: Foundations of Multimodal Data Fusion
Chapter 2: Omics Data Integration in AI Systems
Chapter 3: Neural Networks in Genomic Data Fusion
Chapter 4: Graph-based Methods for Biological Networks
Chapter 5: Single-cell RNA Sequencing and AI
Chapter 6: Phenotypic Data Fusion in Precision Medicine
Chapter 7: Microbiome Data Fusion and Analysis
Chapter 8: Evolutionary Computation in Bioinformatics
Chapter 9: AI-driven Drug Discovery and Repurposing
Chapter 10: Natural Language Processing in Biomedical Literature
Chapter 11: Ethical Considerations in Bioinformatic AI
Chapter 12: Time-series Analysis in Functional Genomics
Chapter 13: Spatial Transcriptomics and Multimodal Fusion
Chapter 14: Automated Machine Learning in Bioinformatics
Chapter 15: Future Trends in Bioinformatics AI Integration
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