Khaled Alsayegh at the King Abdullah International Medical Research Center is creating a Saudi Stem Cell Donor Registry, with 80,000 potential donors identified. The aim is to identify universal donors, reprogram their cells into induced pluripotent stem (iPS) cells, and create a gene bank for matched tissue transplants. Alsayegh is collaborating with Jesper Tegnér at KAUST to create pacemaker cells using single-cell RNA sequencing. Why it matters: This initiative could revolutionize precision medicine in KSA by providing readily available, matched cells for transplants, reducing the need for patient-specific reprogramming and improving treatment outcomes.
MBZUAI researchers collaborated with Carnegie Mellon University and the Broad Institute of MIT and Harvard to develop a new statistical method for analyzing data used for gene regulatory network inference. The method addresses the challenge of distinguishing true zero expression values from dropouts in single-cell RNA sequencing data. This research will be presented at the Twelfth International Conference on Learning Representations (ICLR 2024). Why it matters: Improving gene regulatory network inference can lead to better understanding of disease mechanisms and inform the development of new medicines.
MBZUAI's Assistant Professor of Computational Biology, Eduardo Beltrame, is researching single-cell RNA sequencing to advance personalized medicine. He is also designing MBZUAI’s new master’s and Ph.D. programs in computational biology, set to launch in 2026, under the guidance of Professor Eran Segal. MBZUAI's research agenda includes foundational initiatives like AIDO and the Human Phenotype Project, leveraging vast datasets such as the Emirati Genome Project. Why it matters: This highlights MBZUAI's commitment to cutting-edge research and education in computational biology, positioning it as a potential rival to top global institutions in the field.
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's Eduardo da Veiga Beltrame is developing machine learning tools for analyzing single-cell RNA sequencing data, which measures RNA in thousands of individual cells. Sequencing costs have decreased faster than Moore's Law, enabling large-scale data collection in biology. RNA sequencing provides insights into gene expression and cellular activity, crucial for personalized medicine. Why it matters: Advancements in single-cell RNA sequencing and ML analysis will accelerate personalized medicine by providing detailed insights into cellular mechanisms and disease pathways.