Robert F. Murphy
Ray and Stephanie Lane Center for Computational Biology,
Center for Bioimage Informatics, and Departments of Biological Sciences, Biomedical Engineering, and Machine Learning
Carnegie Mellon University
Biography:
Robert F. Murphy is the Ray and Stephanie Lane Professor of Computational Biology and director of the Ray and Stephanie Lane Center for Computational Biology at Carnegie Mellon University. He also is Professor of Biological Sciences, Biomedical Engineering, and Machine Learning, and Director (with Jelena Kovacevic) of the Center for Biomedical Image Informatics at Carnegie Mellon. He also directs (with Ivet Bahar) the joint CMU-Pitt Ph.D. Program in Computational Biology. Prior to arriving at Carnegie Mellon, Dr. Murphy was a Damon Runyon-Walter Winchell Cancer Foundation postdoctoral fellow with Dr. Charles R. Cantor at Columbia University from 1979 through 1983. Dr. Murphy earned an A. B. in Biochemistry from Columbia College in 1974 and a Ph.D. in Biochemistry from the California Institute of Technology in 1980. He received a Presidential Young Investigator Award from the National Science Foundation shortly after joining the faculty at Carnegie Mellon in 1983. In 2005, NIH selected him as the first full-term chair of its new Biodata Management and Analysis Study Section. In 2006, he was named a Fellow of the American Institute for Medical and Biological Engineering. Dr. Murphy has received research grants from the National Institutes of Health, the National Science Foundation, the American Cancer Society, the American Heart Association, the Arthritis Foundation, and the Rockefeller Brothers Fund. He has co-edited two books and two special journal issues on “Cell and Molecular Imaging,” and published over 140 research papers. He is President-elect of the International Society for the Advancement of Cytometry.
Dr. Murphy’s career has centered on combining fluorescence-based cell measurement methods with quantitative and computational methods. His group at Carnegie Mellon did extensive work on the application of flow cytometry to analyze endocytic membrane traffic beginning in the early 1980’s and pioneered the application of machine learning methods to high-resolution fluorescence microscope images depicting subcellular location patterns in the mid 1990’s. This work led to the development of the first systems for automatically recognizing all major organelle patterns in 2D and 3D images. He currently leads NIH-funded projects for proteome-wide determination of subcellular location in 3T3 cells and continued development of the SLIF system for automated extraction of information from text and images in online journal articles. His group is also responsible for providing image informatics tools for the NIH-funded Technology Center for Networks and Pathways (headquartered at Carnegie Mellon) and for providing structured, image-based information on subcellular location for the National Center for Integrative Biomedical Informatics (headquartered at the University of Michigan).
Dr. Murphy’s leadership experience includes developing the first formal undergraduate program in computational biology in 1987 and founding the Merck Computational Biology and Chemistry program at Carnegie Mellon in 1999. These programs were important forerunners to the 2005 establishment of a Ph.D. program in computational biology in partnership with the University of Pittsburgh. Under his leadership, this program was chosen as one of only ten awardees through the HHMI-NIBIB Interfaces Initiative.
Talk Title: Extraction of Subcellular Location Assertions and Models from Structured and Unstructured Sources
Abstract:An important challenge in the post-genomic era is to identify subcellular location on a proteome-wide basis. A major source of information for this task is imaging of tagged proteins in living cells using fluorescence microscopy. We have previously developed automated systems to interpret the images resulting from such experiments and demonstrated that they can perform as well or better than visual inspection. I will discuss our recent work on extending these approaches to provide comprehensive analysis of subcellular location on a proteome-wide basis. This includes building systems for automated extraction of assertions about protein subcellular location from the combination of text and images in unstructured sources like online journal articles, systems for defining families of proteins that show statistically-indistinguishable subcellular patterns, and systems for training generative models of subcellular location from cell images. I will also describe using real and synthetic images from these systems in cell simulations.