Название: Multimodal Data Fusion for Bioinformatics Artificial IntelligenceАвтор: Umesh Kumar Lilhore, Abhishek Kumar, Narayan Vyas, Sarita Simaiya
Издательство: Wiley-Scrivener
Год: 2025
Страниц: 396
Язык: английский
Формат: pdf (true)
Размер: 16.3 MB
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 applications of Machine Learning are gaining popularity in various fields day by day. Bioinformatics is one of the fields in which automated Machine Learning, AutoML, has great potential in the future. AutoML can be used to create the predictive models and find certain patterns in biological data. Machine learning can be used as a tool for decision making and analysis of biological data. In this work, we have focused on how AutoML pipelines can be integrated into bioinformatics processes and emphasize how it can be used for tasks like drug discovery, protein structure prediction, and sequence analysis. Further, there are a few limitations associated with AutoML in bioinformatics, such as data scarcity, heterogeneous data, interpretability problems, and scalability problems. In conclusion, the future directions and possible developments in AutoML techniques for bioinformatics are discussed, highlighting how the field of Artificial Intelligence and Machine Learning can build sustainable technologies using machine learning for bioinformatics applications.
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.
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