Lab News
March 2024
Boussard Lab Team Walks the Dish
In a recent outing members of the Boussard Lab walked Stanford's The Dish trail, famous for its radiotelescopes and beautiful scenery. Attending lab members (from left to right) included: Behzad Naderalvojoud, Biomedical Informatics Scientist; Siri van der Meijden, Visiting Scholar; and Yuqing Wang, Postdoc; Sara Keller, Visiting Scholar (front, in black); David L. M. Preston, Program Manager (back); Tina Hernandez-Boussard, PI.
Leading the AI Healthcare Revolution
Leading the AI Healthcare Revolution: Insights from California's Policy Discussion
Tina Hernandez-Boussard, PhD, was a featured AI expert at a recent policy discussion in Sacramento on shaping the AI revolution in healthcare care through innovated California policies. This discussion was led by Senator Josh Becker. Other experts included Arpit Davé of Amgen, and Joy Sacmar of Johnson & Johnson Robotics & Digital Solutions. The goal is to provide a framework to navigate challenges and seize opportunities to enhance patient care and drive medical advancements guided by AI.
Recent Publications
Attitudes on Coronary Artery Calcium
Our team utilized an AI pipeline of both supervised NLP models and unsupervised ML techniques to analyze social media discussions about Coronary Artery Calcium (CAC) in order to better describe contemporary attitudes around this risk marker of atherosclerotic cardiovascular disease. This study demonstrates the large potential of an AI-based approach to analyze large, publicly available social media data to generate insights into public perceptions about CAC, which may help guide strategies to improve shared decision-making around ASCVD management and public health interventions.
Sequence Modeling with Evo
In the ground-breaking preprint titled, "Sequence Modeling and Design from Molecular to Genome Scale with Evo,” we discuss how advances in machine learning combined with massive datasets of whole genomes (which encode DNA, RNA, and proteins) enable a biological foundation model that accelerates the mechanistic understanding and generative design of complex molecular interactions. Our team's contribution to this pioneering work encompasses the development of ethical considerations surrounding the application of Evo, a genomic foundation model boasting 7 Billion parameters. These ethical guidelines ensure that the advancements in multi-modal and multiscale learning facilitated by Evo are leveraged responsibly. Through our involvement, we highlight the importance of ethical practices in the cutting-edge domain of genomic modeling, ensuring that the potentials of Evo are realized with utmost consideration for ethical implications.