KAUST alumnus Dimitrios Kleftogiannis (Ph.D. '16) is now a cancer researcher at the University of Bergen, Norway, using bioinformatics to study liquid biopsies for cancer research. He transitioned from computer science to bioinformatics after his Ph.D. and was inspired by Prof. Mel Greaves at the Institute of Cancer Research in London. Why it matters: This highlights the impact of interdisciplinary training at KAUST and its alumni's contributions to applying AI and computational methods to advance healthcare research.
This article discusses the use of artificial intelligence in precision oncology, particularly in understanding individual tumor mechanisms and aiding clinical decision-making. Dr. Xinghua Lu, with extensive experience in medicine and biomedical informatics, will present research on individualized Bayesian causal inference methods for investigating oncogenic mechanisms. These methods aim to provide clinical decision support at the cellular, tumor, and patient levels. Why it matters: AI-driven precision oncology can enable more personalized and effective cancer treatments, improving patient outcomes in the region and globally.
KAUST's Sciencetown podcast episode 23 features researcher Dana Al-Sulaiman discussing portable biosensing technologies for cancer detection. These devices aim to enable liquid biopsies for early screening and personalized treatment. The biosensors gather clinical information from biological samples to inform clinical decisions. Why it matters: This research can advance non-invasive diagnostics and personalized medicine in the region.
KAUST researchers developed a statistical approach to improve the identification of cancer-related protein mutations by reducing false positives. The method uses Bayesian statistics to analyze protein domain data from tumor samples, accounting for potential errors due to limited data. The team tested their method on prostate cancer data, successfully identifying a known cancer-linked mutation in the DNA binding protein cd00083. Why it matters: This enhances the reliability of cancer research at the molecular level, potentially accelerating the discovery of new therapeutic targets.
MBZUAI researchers are refining AI techniques to improve cancer diagnosis for colorectal and breast cancer, both common in the Middle East. They are using "few-shot tissue image generation," in which AI generates data for training AI models to recognize lesions, addressing the challenge of limited training data. The developed framework improves the efficiency of radiologists in breast cancer diagnosis, leading to better detection of breast lesions and timely treatment interventions. Why it matters: These advancements in AI-aided diagnostics can lead to earlier and more accurate cancer detection, ultimately improving patient outcomes in the region and beyond.
Eduardo da Veiga Beltrame, bioinformatics lead at ImYoo (a Caltech spinout), presented on scalable methods for single-cell omics data analysis, including kallisto|bustools and scvi-tools. He highlighted their use in ImYoo's decentralized longitudinal study on Inflammatory Bowel Disease (IBD), where patients self-collect capillary blood samples. Beltrame also discussed his research on STEM education programs in Brazil as a visiting scholar at UC Berkeley. Why it matters: This highlights the growing trend of decentralized clinical studies leveraging advanced single-cell technologies for precision medicine, showcasing the potential of remote data collection and analysis in understanding complex diseases.
KAUST held a "Run for a Cure" charity race on October 28 for breast cancer research, with over 425 participants from KAUST and partner organizations. A KAUST Ph.D. student discussed her research on non-invasive early cancer detection using plasma blood samples. The event included 10K, 5K, and 3K runs through KAUST, aligning with Vision 2030's goal of increasing public participation in sports. Why it matters: This event highlights KAUST's commitment to healthcare research, community engagement, and supporting national goals for health and sustainability.
Petar Stojanov from the Broad Institute of MIT and Harvard will give a talk on cancer data analysis, covering the fundamentals of cancer, the nature of large-scale data collected, and main analysis objectives. The talk will also address open questions in cancer data analysis and how machine learning and generative modeling can help. Stojanov's research focuses on applying machine learning to genomic analysis of cancer mutation and single-cell RNA sequencing data. Why it matters: Applying AI and machine learning to cancer research can lead to a better understanding of the disease and development of new therapies.