Machine Learning in Medicine (MLxMed) Seminar Series

Speaker: Arjun Krishnan, PhD, Associate Professor, Department of Biomedical Informatics and the Center for Health Artificial Intelligence, University of Colorado Anschutz Medical Campus

Abstract: Publicly available massive data collections contain valuable signals that can help fill critical gaps in our biomedical knowledge about the molecular mechanisms underlying complex traits and diseases. Our group’s goal is to enable biomedical researches to effectively reuse these data — e.g., omics profiles, molecular networks, knowledgebases, unstructured text corpora, and genetic associations — to gain nuanced insights into the heterogeneous traits/disease. We do so by developing integrative machine learning approaches and tools that work to improve every stage of data-driven biology: harmonizing and integrating heterogeneous genomic and genetic data, reconstructing genome-scale networks for data and knowledge representation, transferring information across species, natural language processing to annotate omics data, and developing open software and interactive webservers. The approaches that we develop are highly general, thus applicable to a wide range of biological phenomena in both human and model organisms. In this talk, I will provide an overview of our work in these areas by highlighting some specific projects.

Hosted by the Department of Biomedical Informatics, University of Pittsburgh; the Hariri Institute for Computing, Boston University; and the University of Toronto

If you have any questions about the event or have zoom issues, please contact Maria Bond at bond@pitt.edu.

For attendance details and more information, please visit: http://ml-in-medicine.org/