Pierre Baldi from UC Irvine presented applications of AI to biomedicine, covering molecular-level analysis of circadian rhythms, real-time polyp detection in colonoscopy videos, and prediction of post-operative adverse outcomes. He discussed integrating AI in future AI-driven hospitals. The presentation was likely part of a panel discussion hosted by MBZUAI in collaboration with the Manara Center for Coexistence and Dialogue. Why it matters: This highlights the growing interest in AI applications within the healthcare sector in the UAE, particularly through institutions like MBZUAI.
MBZUAI hosted a two-day workshop on "Big Model AI in Drug Design" starting February 20, 2023. The workshop featured presentations from researchers in public and private institutions working on AI and health. MBZUAI Adjunct Professor Eran Segal opened the workshop with a talk on the Human Phenotype Project. Why it matters: The event highlights the growing interest and activity in applying AI, particularly large models, to advance drug discovery and personalized medicine within the UAE's research ecosystem.
The AI4Bio Workshop at MBZUAI explored the intersection of AI and biology, focusing on AI-driven virtual organisms and foundation models. Eric Xing presented his vision of using AI to simulate biological activities, offering a safer alternative to physical experiments. Researchers like Le Song and Jen Philippe Vert are developing foundation models for biological systems, enhancing drug discovery and bioengineering. Why it matters: This signals the growing importance of AI in advancing biological research and healthcare innovation within the UAE and globally.
MBZUAI Visiting Professor Haiyan Huang is working on bridging biology and AI by incorporating domain knowledge into modeling frameworks. She combines statistical principles, AI tools, and domain expertise to develop scientifically informed and statistically grounded methods. Her work addresses the challenge of extracting meaningful signals from complex biological data.
Researchers at the Rosalind Franklin Institute are using generative AI, including GANs, to augment limited biological datasets, specifically mirtron data from mirtronDB. The synthetic data created mimics real-world samples, facilitating more comprehensive training of machine learning models, leading to improved mirtron identification tools. They also plan to apply Large Language Models (LLMs) to predict unknown patterns in sequence and structure biology problems. Why it matters: This research explores AI techniques to tackle data scarcity in biological research, potentially accelerating discoveries in noncoding RNA and transposable elements.
Dr. Min Xu joins MBZUAI as Affiliated Assistant Professor in Computer Vision to advance AI-based biomedical image analysis. His research focuses on cellular cryo-electron tomography (Cryo-ET) 3D image analysis, spatial transcriptomics, digital pathology, and automated science. Xu will collaborate with MBZUAI faculty and advise master’s students, leveraging his expertise in computational biology and bioinformatics. Why it matters: This appointment strengthens MBZUAI's capabilities in applying AI to critical areas of biomedical research, potentially leading to breakthroughs in disease understanding and treatment.
MBZUAI Visiting Professor Haiyan Huang is working on bridging biology and AI by incorporating domain knowledge into modeling frameworks. She combines statistical principles, AI tools, and domain expertise to develop scientifically informed and statistically grounded methods. Her work addresses the challenge of extracting meaningful signals from complex biological data. Why it matters: This interdisciplinary approach can lead to more accurate and useful AI models for biological research and healthcare applications in the region.
MBZUAI Professor Kun Zhang is working on applying AI to understand cause-and-effect relationships in biology, with the goal of accelerating scientific discovery and improving human health. He aims to develop foundation models for biology that can process diverse data types and provide insights into the causes and treatments of health problems. These models could help scientists develop new medicines and preventative measures for diseases. Why it matters: This research has the potential to significantly advance the field of medicine by enabling a deeper understanding of the complex biological processes that underlie disease.