UC San Diego: UC San Diego Joins NIH ‘Bridge to Artificial Intelligence’ Program

Researchers at University of California San Diego School of Medicine have been selected to lead components of the National Institutes of Health (NIH) Common Fund’s Bridge to Artificial Intelligence (Bridge2AI) program. Over the next four years, Bridge2AI will award $130 million to accelerate the widespread use of AI in biomedical research and health care.

Physicians and scientists have long recognized the potential of AI to help understand and treat disease, but its use in clinical and research settings remains limited. This is, in part, because AI tools cannot always be easily or appropriately applied to new datasets that were not organized for this type of analysis. Furthermore, most AI algorithms function as “black boxes” — when they reach a conclusion about something, no one knows exactly how or why that decision was made.

To address these issues, Bridge2AI will fund four Data Generation Projects to create comprehensive AI-ready datasets that will lay the groundwork for new, interpretable and trustworthy AI technologies. The four multi-site projects will be unified by the Bridge Center, an executive hub that oversees the integration, dissemination and evaluation of all Bridge2AI activities.

Trey Ideker, PhD, professor at UC San Diego School of Medicine, will serve as principal investigator for one of the Data Generation Projects. Lucila Ohno-Machado, MD, PhD, professor and associate dean for informatics and technology at UC San Diego School of Medicine, will serve as a principal investigator for the Bridge Center.

“This is the first time NIH has invested in biomedical AI at this scale, and we are thrilled to be a part of it,” said Ideker. “UC San Diego has proven itself to be a pioneer in clinical and research AI technology, but this funding will help cement our place in the AI revolution.”

Cell Maps for AI
Ideker and collaborators are expected to receive nearly $20 million in the next four years to launch Cell Maps for AI, a research project designed to usher in a new era of precision medicine. The team envisions a future in which an AI algorithm could analyze a patient’s genome and decipher which disease they have, what stage they are in and which treatments are most likely to help. Importantly, they say the algorithm must be interpretable, such that a physician could point to the molecular and cellular pathways that inform its decisions.

“It’s not enough for an algorithm to just take a complex set of mutations and decide what drug to give a patient if we don’t know why it’s making that choice,” said Ideker. “We may now have enough human genomes sequenced to power precision medicine, but what we don’t have yet is a clear map of cellular biology to interpret the data with.”

To address this, the project aims to map the structure and function of a human cell in its entirety, starting with the most basic cell type: the stem cell. The researchers will obtain induced pluripotent stem cells from a variety of genetic backgrounds and combine microscopy, biochemistry and computational tools to study their biology at multiple scales. The final product will be a comprehensive model of the cell, from genes and proteins to entire organelles and how they all work together. Once the stem cell has been modeled, they plan to use the same approach to model other cells, such as those that are dividing, differentiating or in various disease states.

Their goal is to eventually have a library of cell maps across many demographic and disease contexts, which can be used to train AI algorithms to make informed and interpretable decisions about human health.

“With Bridge2AI, we are not only generating unprecedented datasets, but also developing a system to do this work in an organized and ethical way, which will set the field up for future success,” Ideker said.

Additional principal investigators on the project include Prashant Mali, PhD, at the UC San Diego Jacobs School of Engineering and researchers at UC San Francisco, Stanford University, University of Alabama, University of Alabama at Birmingham, University of Montreal, Simon Fraser University, University of South Florida, University of Texas at Austin, University of Virginia and Yale University.

Bridge Center
Coordinating all program efforts is the Bridge Center, consisting of six cores focused on Administration, Ethics, Teaming, Standards, Tool Optimization and Skills and Workforce Development. UC San Diego is expected to receive nearly $10 million over four years to lead both the Administrative and Ethics Cores.

“Leading the Bridge Center is exciting in many ways,” said Ohno-Machado. “In addition to supporting this groundbreaking and interdisciplinary research, we will also spearhead new models of scientific collaboration, ensure the training of a highly diverse group of researchers and ultimately build AI tools that are truly applicable to everyone.”

Ohno-Machado will lead the Bridge Center Administrative Core and co-lead the Ethics Core with Camille Nebeker, EdD, at the Herbert Wertheim School of Public Health and Human Longevity Science at UC San Diego. Other UC San Diego investigators include Cinnamon Bloss, PhD, also at the Herbert Wertheim School of Public Health, Tsung-Ting Kuo, PhD, UC San Diego School of Medicine, and Jingbo Shang, PhD, Babak Salimi, PhD, and Berk Ustun, PhD, all at Jacobs School of Engineering and the Halıcıoğlu Data Science Institute. Additional Bridge Center collaborators include faculty at the Broad Institute, Vanderbilt University and University of Texas Health.

Nebeker, along with UC San Diego School of Medicine faculty Sally Baxter, MD, and Linda Zangwill, PhD, are also leading modules within another Data Generation Project called AI Ready and Equitable Atlas for Diabetes Insights (AI-READI), led by principal investigators at University of Washington. The project will generate an ethically sourced data repository to develop machine learning models with the goal of learning how Type 2 diabetes is influenced by patients’ genes, lifestyle and environments.

“Generating high-quality ethically sourced datasets is crucial for enabling the use of next-generation AI technologies that transform how we do research,” said Lawrence A. Tabak, DDS, PhD, who is performing the duties of the Director of NIH. “The solutions to long-standing challenges in human health are at our fingertips, and now is the time to connect researchers and AI technologies to tackle our most difficult research questions and ultimately help improve human health.”