Our latest chapter/article published in the book i.e., Artificial Intelligence for Drug Product Lifecycle Applications (1st Edition), edited by Drs. Alberto Pais, Carla Vitorino, Sandra Nunes, and Tânia Cova i.e., Multi-omics/genomics in predictive and personalized medicine
Abstract:
The quest to understand the causes of common and rare traits has been the central focus of humankind, since the beginning of scientific discovery. Since our understanding of the complex nature of disease has evolved, we now realize that a precision medicine approach is necessary to optimally diagnose and determine the best treatment for individual patients. Well integrated and interpreted multi-omics/genomics and multivariate clinical data have the potential to provide better predictive value than any of these data types alone to determine predisposition and diagnostic biomarkers. It will support in establishing relationships between genomics variations and disease mechanisms. However, despite current progress, multi-omics/genomic data has not been widely adopted on a regular basis in the clinical settings to help practitioners in providing precise medical treatment. In this chapter, we discuss the importance of Findable, Accessible, Intelligent, and Reproducible (FAIR) approach to facilitate implementation of precision medicine. Furthermore, we propose to utilize artificial intelligence and machine learning approaches to identify novel biomarkers and predict disease with high accuracy.
Publication/Citation:
- Ahmed, Z. (2025). Multi-omics/genomics in predictive and personalized medicine (Chapter 5). Artificial Intelligence for Drug Product Lifecycle Applications (1st Edition). Editors: Alberto Pais, Carla Vitorino, Sandra Nunes, Tânia Cova. Paperback ISBN: 9780323918190, Academic Press. (Elsevier).