Marinka Zitnik, PhD

AI for Medicine: The state of a person is described with increasing precision incorporating modalities like genetic code, cellular atlases, molecular datasets, and therapeutics—the challenge is how to reason over these data to develop powerful disease diagnostics and empower new kinds of therapies. Our research creates new avenues for fusing knowledge and patient data to give the right patient the right treatment at the right time and have medicinal effects that are consistent from person to person and with results in the laboratory. AI for Science: For centuries, the method of discovery—the fundamental practice of science that scientists use to explain the natural world systematically and logically—has remained largely the same. We are using AI to change that. The natural world is interconnected, from the various facets of genome regulation to the molecular and organismal levels. These interactions across different levels yield a bewildering degree of complexity. Our research seeks to disentangle this complexity, developing AI models that advance drug design and help develop new kinds of therapies. Current research directions in the lab include: - Large pre-trained AI to fuse data modalities like genetic code, single-cell atlases, molecular datasets, and therapeutics via multimodal knowledge graph networks and large pretrained language models - Geometric deep learning and graph neural networks to reason about network biology and medicine - Multi-scale, individualized, and contextualized AI to transfer prediction prowess acquired from one data type to another and to design contextually adaptive AI that can dynamically adjust outputs to biological contexts, such as patients, diseases, and cell types, in which they operate - Better methods for drug design to enhance drug development across therapeutic modalities and stages of development - Foundations for scientific discovery in the age of AI.

Generalizable AI predicts immunotherapy outcomes across cancers and treatments. Authors: Shen W, Nguyen TH, Li MM, Huang Y, Moon I, Nair N, Marbach D, Zitnik M. medRxiv 2025-05-05 Embedding Methods for Electronic Health Record Research. Authors: Kauffman J, Miotto R, Klang E, Costa A, Norgeot B, Zitnik M, Khader S, Wang F, Nadkarni GN, Glicksberg BS. Annu Rev Biomed Data Sci 2025-05-01 A Causality-Aware Paradigm for Evaluating Creativity of Multimodal Large Language Models. Authors: Huang Z, Zhong S, Zhou P, Gao S, Zitnik M, Lin L. IEEE Trans Pattern Anal Mach Intell 2025-05-01 ATOMICA: Learning Universal Representations of Intermolecular Interactions. Authors: Fang A, Zhang Z, Zhou A, Zitnik M. bioRxiv 2025-04-08 Digital twins as global learning health and disease models for preve