AI and Machine Learning in Genomics
Helmholtz Munich and the Eric and Wendy Schmidt Center at the Broad Institute launch Collaborative Research Initiative
Helmholtz Munich and the Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard announced the launch of a collaboration to bridge a gap in health research with AI and machine learning.
In the past decade, the field of genomics has accelerated to a point where we can now both measure and perturb biological systems at massive, unprecedented scales, holding huge potential for disease treatment. However, the computational tools needed to take advantage of all this data have not kept pace. By leveraging machine learning methods, the partnership between Helmholtz Munich and the Eric and Wendy Schmidt Center seeks to gain valuable insights into important genomics problems while simultaneously advancing the foundations of machine learning through novel research inspired by genomics questions.
Leading this joint initiative are Prof. Caroline Uhler, co-director of the Eric and Wendy Schmidt Center at the Broad Institute, and Prof. Fabian Theis, Head of the Computational Health Center (CHC) at Helmholtz Munich and Director of Helmholtz AI. Both Caroline Uhler and Fabian Theis have backgrounds in machine learning, statistics, data science, and human biology. “This exchange model between the Broad Institute and Helmholtz Munich will merge our expertise on machine learning and genomics to foster innovative ways to address major challenges in biomedical research,” declares Fabian Theis.
The collaboration will encompass a range of activities, including the exchange of graduate students, postdoctoral fellows, and other research staff between the two research centers. These individuals will undertake short research stays, enabling them to benefit from the expertise and resources available at both centers. In addition, the research centers will co-organize workshops and conferences to facilitate knowledge exchange and foster collaboration in the field of AI and genomics.
“Despite an explosion in biological data, the technology sector remains the key driver of machine learning advances today,” said Caroline Uhler. “Both Helmholtz Munich and the Broad Institute are seeking to change that by developing foundations of machine learning that are geared specifically to biological problems, and we’re excited for this collaboration to amplify our efforts.”
About the scientists
Prof. Dr. Dr. Fabian Theis, Head of the Computational Health Center, Director of the Institute of Computational Biology at Helmholtz Munich and Director of Helmholtz AI.
Prof. Dr. Caroline Uhler, Professor at the Department of Electrical Engineering and Computer Science and the Institute for Data, Systems and Society at MIT, and Co-Director of the Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard
Find out more about research at the Computational Health Center at Helmholtz Munich here
Learn more about Helmholtz AI
Check out the Eric and Wendy Schmidt Center at Broad Institute here
About the Eric and Wendy Schmidt Center
The Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard is enabling a new field of interdisciplinary research at the intersection of machine learning and biology, aimed at improving human health. We seek to make the biological questions of our time key drivers for foundational advances in machine learning — and for ML to drive what kinds of data biologists generate. In addition to a cohort of graduate and postdoctoral fellows, the center includes collaborators from across the Broad community, the wider Cambridge ecosystem, as well as from around the world.
About Helmholtz Munich
Helmholtz Munich is committed to unraveling the intricate connections between environmental factors, genetic background, and lifestyle choices that impact human health. Through interdisciplinary research, their research centers shed light on the biological processes underlying diseases. The Computational Health Center, under the guidance of Fabian Theis, focuses on the development of applied artificial intelligence (AI), integrative machine learning, and mechanistic system models. By integrating information across various scales, ranging from single-cell level to population-level data, the center aims to advance precision medicine and drive biomedical discovery.