WIM no. 17 Spring 2020

מכון ויצמן למדע access and manipulation that makes it possible to “scan” population-wide data. He is the director of a unique collaboration between the Weizmann Institute of Science and Clalit Health Systems, Israel’s largest HMO. The Weizmann/Clalit project (and its Bench-to-Bedside Program, directed by Prof. Gabi Barbash, a physician and former Director General of Israel’s Ministry of Health), makes available for scientific research more than 20 years of data, comprising computerized records of lab tests, treatments, and results for over four million Israeli citizens. This data repository, based on anonymized patient records, is being analyzed using AI machine learning protocols, as well as insights from the emerging research field of data science. The ability of AI to recognize patterns within huge data sets has helped Prof. Tanay and his colleagues identify previously unrecognized factors that play a role in human health. For example, in collaboration with Dr. Liran Shlush of the Department of Immunology, Prof. Tanay established an AI-based strategy for the early diagnosis of Acute Myeloid Leukemia (AML). Based on the Clalit medical records, deep sequencing of the genes recurrently mutated in AML, and machine learning, the scientists identified a distinct gene mutation profile that accurately predicted which patients would live to a healthy old age, without developing the disease—a model that could potentially be used to identify pre-AML risk many years prior to disease onset. In another recent AI achievement, Prof. Eran Segal— who holds appointments in both the Department of Molecular Cell Biology and the Department of Computer Science and Mathematics—designed an algorithm that can predict the risk of gestational diabetes even before pregnancy. This advance— based on machine learning algorithms that revealed clinically significant patterns in the Clalit data—may one day allow doctors to prevent gestational diabetes in specific patients, by prescribing lifestyle interventions for reducing high blood sugar. If Prof. Tanay has his way, the next AI-based health discoveries may emerge from closer collaboration between scientists and physicians. The Tanay team is now putting the finishing touches on a new interface that would allow doctors—with no training in AI or machine learning—to query the Clalit database, test out their hypotheses, and provide better, more personalized care for their patients. AI and climate research A new AI-based strategy based on the work of three researchers, including Prof. Ilan Koren of the Department of Earth and Planetary Sciences—is using machine learning to achieve an unprecedented understanding of how cloud formation mediates the Earth’s energy balance and water cycle, and influences climate. Every climate model must take clouds into account, but such data is usually gathered by satellites that capture low-resolution images that miss many small clouds, and reveal only cloud systems’ most basic properties. Prof. Koren, on the other hand, is developing a completely different approach to cloud analysis that, with the help of AI, will generate a wealth of new climate data. Prof. Koren’s strategy, called Cloud Tomography, uses medically inspired CT algorithms to enable a coordinated fleet of 10 tiny satellites, each the size of a shoebox, to gather images of clouds’ external and internal 3D structures, as well as the size and concentration of water droplets within them. A scientific space mission—called CloudCT—will target small cloud fields that are often missed by remote-sensing technologies and, it is hoped, will resolve some of the unknowns surrounding climate prediction. After the satellites are launched into orbit, they will adopt the formation of a continuously moving and networked satellite “swarm” spread over hundreds of kilometers. The satellites will gather images from various points within cloud fields simultaneously, and transmit images to the ground, allowing scientists to derive 3D information about how such clouds influence, and respond to, changing environmental conditions. With the help of machine learning, Prof. Koren and his colleagues will be able to identify very complex interactions, with a special emphasis on the smaller cloud structures that temper climate and can also be very sensitive to climate change. The new system is expected to improve the accuracy of current climate models. AI and the nature of the universe Machine learning methods are designed to explore and independently analyze large data sets. And if you’re looking for large data sets, then particle physics—a discipline in which scientists examine Weizmann MAGAZINE Science Feature