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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.

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Multimodal Data Fusion for Bioinformatics AI

Multimodal Data Fusion for Bioinformatics AI

Publisher: Wiley

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 Deadline30 December 2023
Abstract Acceptance Notification30 January 2024
Full Chapter Submission Deadline10 March 2024
Chapter Acceptance Notification10 May 2024
Projected Book Release DateDecember 2024
Important Guidelines
Citation StyleAPA (7th Edition)
Formatting11 pt Times Roman, 1.5 line spacing
OriginalityPlagiarism Under 10%, 0% AI Generated Content
  • Heading 1: ALL BOLD CAPS
  • Heading 2: Bold Title Case
  • Heading 3: Bold Italic Title Case
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  • Paragraphs: Should be Numbered (1.1, 1.1.1, etc.)
Chapter submissions are closed. Thank you for your cooperation.
  • 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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