Graph Neural Networks for Biomedical Data

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• Marinka Zitnik, Harvard University
• Wednesday 25 November 2020, 15:00-16:00
• Zoom.

If you have a question about this talk, please contact Mateja Jamnik.

Note the change of date and time. This seminar is part of the Wednesday seminar series too.

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The success of machine learning depends heavily on the choice of representations used for downstream tasks. Graph neural networks have emerged as a predominant choice for learning representations of networked data. Still, methods require abundant label information and focus either on nodes or entire graphs. In this talk, I describe our efforts to expand the scope and ease the applicability of graph representation learning. First, I outline SubGNN, the first subgraph neural network for learning disentangled subgraph representations. Second, I will describe G-Meta, a novel meta-learning approach for graphs. G-Meta uses subgraphs to generalize to completely new graphs and never-before-seen labels using only a handful of nodes or edges. G-Meta is theoretically justified and scales to orders of magnitude larger datasets than prior work. Finally, I will discuss applications in biology and medicine. The new methods have enabled the repurposing of drugs for new diseases, including COVID -19, where our predictions were experimentally verified in the wet laboratory. Further, the methods enabled discovering dozens of combinations of drugs safe for patients with considerably fewer unwanted side effects than today’s treatments. The methods also allow for molecular phenotyping, much better than more complex algorithms. Lastly, I describe our efforts in learning actionable representations that allow users of our models to receive predictions that can be interpreted meaningfully.

BIO: Marinka Zitnik is an Assistant Professor at Harvard University with appointments in the Department of Biomedical Informatics, Blavatnik Institute, Broad Institute of MIT and Harvard, and Harvard Data Science. Dr. Zitnik is a computer scientist studying machine learning, focusing on challenges brought forward by data in science, medicine, and health. She has published extensively on representation learning, knowledge graphs, data fusion, graph ML (NeurIPS, JMLR , IEEE TPAMI , KDD, ICLR ), and applications to biomedicine (Nature Methods, Nature Communications, PNAS ). Her algorithms are used by major institutions, including Baylor College of Medicine, Karolinska Institute, Stanford Medical School, and Massachusetts General Hospital. Her work received several best paper, poster, and research awards from the International Society for Computational Biology. She has recently been named a Rising Star in Electrical Engineering and Computer Science (EECS) by MIT and also a Next Generation in Biomedicine by the Broad Institute, being the only young scientist who received such recognition in both EECS and Biomedicine.

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