KAUST organized a breast cancer awareness event in Thuwal on October 12, attended by over 150 women and girls from the local community, along with healthcare and education partners. The event featured educational lectures, personal stories from breast cancer survivors, and interactive sessions on early screening. KAUST's director of Social and Community Development highlighted the university's commitment to women's health and empowerment through such initiatives. Why it matters: This event demonstrates KAUST's commitment to social responsibility and community engagement by promoting health awareness and empowering women, aligning with Saudi Vision 2030.
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.
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.
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 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.
KAUST will host its Fall Discovery Enrichment program from October 14-22, focusing on the theme "Food for All." The program includes discussions on women in biology led by Bettina Berger, Jasmeen Merzaban, Peiying Hong, and Ashwag Albukhari. Other activities feature a workshop on diet improvement by Amna Malik and cooking demonstrations by German chef Bernd Arold, alongside screenings of food-related movies. Why it matters: The event promotes community engagement and education around food-related topics, highlighting the intersection of science, health, and culture within the KAUST community.
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.