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.
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.
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 master's student Sayed Hashim is applying machine learning to improve cancer diagnosis and treatment, motivated by personal loss. He and fellow student Muhammad Ali developed algorithms for cancer type classification from multi-omics data, achieving over 96% accuracy. Their work, supervised by MBZUAI faculty, resulted in a published paper on multi-omics data representation learning. Why it matters: This research demonstrates the potential of AI and machine learning to advance cancer research and personalized medicine in the region.
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.
MBZUAI's first Ph.D. graduate, Numan Saeed, developed deep learning models to diagnose head and neck cancers using PET and CT scan imagery. His research focused on improving early detection and accurate localization of tumors, aiming to enhance diagnosis and prognosis. Early diagnosis can reduce mortality rates by up to 70%. Why it matters: This research showcases the potential of AI in healthcare to improve cancer diagnosis and treatment, addressing a critical need in resource-constrained healthcare systems.
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.