Multimodal data fusion for bioinformatics artificial intelligence

• Titel: Multimodal data fusion for bioinformatics artificial intelligence
• Mitwirkende: Lilhore, Umesh Kumar [HerausgeberIn]
• Mitwirkende: Vyas, Narayan [HerausgeberIn]
• Verf.angabe: edited by Umesh Kumar Lilhore... [and 4 others]
• Verlagsort: Hoboken, NJ ; Beverly, MA
• Verlagsort: Hoboken, NJ ; Beverly, MA
• Verlag: John Wiley & Sons, Inc.
• Verlag: Scrivener Publishing LLC
• Jahr: 2025
• Jahr: 2025
• Umfang: 1 online resource
• Fussnoten: Description based on online resource; title from digital title page (viewed on March 18, 2025)
• ISBN: 978-1-394-26995-2
• ISBN: 1-394-26995-1
• ISBN: 978-1-394-26996-9
• ISBN: 1-394-26996-X
• ISBN: 978-1-394-26994-5
• ISBN: 1-394-26994-3
• ISBN: 978-1-394-26993-8
• Datenträger: Online-Ressource
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe
• Sach-SW: Bio-informatique
• Sach-SW: Intelligence artificielle ; Applications biologiques
• Sach-SW: Intelligence (AI) & Semantics
• Sach-SW: COMPUTERS
• K10plus-PPN: 1921171324
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4696552179
• Lokale URL Inst.: Zum Volltext

Abstract

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.