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.
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.
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.
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 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.
MBZUAI researchers developed Human-in-the-Loop for Prognosis (HuLP), a new AI system designed to help physicians assess cancer progression by providing information about its predictions and allowing user intervention. The system aims to foster collaboration between physicians and AI, rather than replacing doctors. It was presented at the 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). Why it matters: This research highlights the potential of AI to augment physician expertise in critical areas like cancer prognosis, improving patient care and treatment decisions.
Researchers at MBZUAI have developed a new machine learning method called survival rank-n-contrast (SurvRNC) to improve survival models for cancer prognoses. The method is designed to predict survival times for head and neck cancer patients using multimodal data while accounting for censored data (missing values). Numan Saeed presented the team’s work at the 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). Why it matters: Accurate prognoses can significantly improve patient outcomes, and this research contributes to advancements in machine learning techniques for handling complex and incomplete medical data.
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.