KAUST alumna Sara Althubaiti (M.S. '18) is now a computer science Ph.D. student in the Bio-Ontology Research Group, focusing on using AI to prioritize cancer mutations and predict new disease treatments. Her work involves understanding the relationship between drug side effects and disease phenotypes. Althubaiti aims to continue in academia after her Ph.D., contributing to research in Saudi universities. Why it matters: This highlights KAUST's role in fostering local talent and contributing to advancements in AI-driven healthcare research within the Kingdom.
Søren Brunak presented deep learning approaches for analyzing disease trajectories using data from 7-10 million patients in Denmark and the USA. The models predict future outcomes like mortality and specific diagnoses, such as pancreatic cancer, using 15-40 years of patient data. Disease trajectories and explainable AI can generate hypotheses for molecular-level investigations into causal aspects of disease progression. Why it matters: This research demonstrates the potential of large-scale patient data and AI to improve disease prediction and generate hypotheses for further investigation into disease mechanisms relevant to regional healthcare systems.
Carlo Maj from the University of Marburg will discuss using polygenic modeling to analyze the genetic architecture of multifactorial traits. He will present how these approaches can be used to predict the genetically driven components of complex phenotypes. The talk highlights the potential of these methods to bridge genomic research and genetic epidemiology using biobank data. Why it matters: Such methods could improve disease risk assessment and advance personalized risk management in the region if applied to local biobanks or datasets.