Russ Altman

Russ Altman, MD, PhD, Kenneth Fong Professor, Departments of Bioengineering, Genetics (Primary), Departments of Medicine, Biomedical Data Science (Secondary), Department of Computer Science (Courtesy), Stanford University

Research Description: My primary interest is the use of computing technologies to understand the molecular basis of disease, including informatics, data science and AI. Our primary application area is in understanding the causes for variation in drug response. We have worked on pharmacogenetics (the genetic influences on drug response, I founded the original PharmGKB resource), epigenetics, disease comorbidity, environmental influences and others sources of variability in drug response. Recently, my lab has been focusing on the use of AI to understand molecular and cellular pathways and structures. This includes creating novel AI models of 3D structure, cellular structure and thinking about what the constituents of a Virtual Cell would be and how they could be constructed. We are also interested in using AI to recognize disease in biobanks (primarily the UK Biobank and All of US), and have published some novel methods for recognizing and predicting phenotypes that may not be coded or even recognized by clinicians. Going forward, we are interested in understanding the pleiotropic effects of drugs, and diabetes drugs represent a major target for this kind of analysis, since they clearly have effects well beyond glucose metabolism, with influences on (and from) the immune system, musculoskeletal system, cardiovascular system, renal system and likely every other human organ system. Using AI and machine learning, we hope to build models to understand unexpected interactions with pathways relevant to these other systems, and potentially the discovery of entirely new pathways using the capabilities of generative AI.

Selected relevant publications (Stanford DRC members are in BOLD, note #5 is an abstract with a manuscript under review) 

  1. Coassolo L, Liu T, Jung Y, Taylor NP, Zhao M, Charville GW, Nissen SB, Yki-Jarvinen H, Altman RB, Svensson KJ. Mapping transcriptional heterogeneity and metabolic networks in fatty livers at single-cell resolution. iScience. 2022 Dec 15;26(1):105802. doi: 10.1016/j.isci.2022.105802. PMID: 36636354; PMCID: PMC9830221.