Prof. Dr. Robert Murphy
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 and he received an Alexander von Humboldt Foundation Research Award in 2008. 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 150 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 1980Õs 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.
- T.E. Buck, J. Li, G.K. Rohde, R.F. Murphy: Towards the virtual cell: Automated approaches to building models of subcellular organization 'learned' from microscopy images. Bioessays, 2012; 34:791-799
- K.W. Eliceiri, M.R. Berthold, I.G. Golberg, L. Ibanez, B.S. Manjunath, M.E. Martone, R.F. Murphy, H. Peng, A.L. Plant, B. Roysam, N. Stuurmann, J.R.Swedlow, P. Tomancak, A.E. Carpenter: Biological Imaging Software Tools. Nature Methods , 2012; 9:697-710
- B.H. Cho, I. Cao-Berg, J.A. Bakal, R.F. Murphy: OMERO.searcher: Content-based image search for microscope images. Nature Methods , 2012; 9:633-634
- J. Li, L. Xiong, J. Schneider, R.F. Murphy: Protein Subcellular Location Pattern Classification in Cellular Images Using Latent Discriminative Models. Bioinformatics , 2012; 28, i32-39
- R.F. Murphy: CellOrganizer: Image-derived Models of Subcellular Organization and Protein Distribution.Methods in Cell Biology , 2012; 110: 179-193
- C. Jackson, E. Glory, R.F. Murphy, J. Kovacevic: Model building and intelligent acquisition with application to protein subcellular location classification.Bioinformatics, 2011; 27:1854-1859
- R.F. Murphy: An active role for machine learning in drug development Nat Chem Biol, 2011; 7 (6): 327-330
- T.H. Lin, Z. Bar-Joseph, R.F. Murphy: Learning Cellular Sorting Pathways Using Protein Interactions and Sequence Motifs Research in Computational Molecular Biology, 2011; 6577: 204-221
- T.H. Lin TH, R.F. Murphy, Z. Bar-Joseph: Discriminative Motif Finding for Predicting Protein Subcellular Localization Ieee Acm T Comput Bi, 2011; 8 (2): 441-451
- T. Peng, R.F. Murphy: Image-derived, Three-dimensional Generative Models of Cellular Organization Cytom Part A, 2011; 79A (5): 383-391
- L.P. Coelho, T. Peng, R.F. Murphy: Quantifying the distribution of probes between subcellular locations using unsupervised pattern unmixing Bioinformatics, 2010; 26 (12): i7-i12
- T. Peng, G.M.C. Bonamy, E. Glory-Afshar, D.R. Rines, S.K. Chanda, R.F. Murphy: Determining the distribution of probes between different subcellular locations through automated unmixing of subcellular patterns PNAS, 2010; 107 (7): 2944-2949
- Y. Hu, E. Osuna-Highley, J.C. Hua, T.S. Nowicki, R. Stolz, C. McKayle, R.F. Murphy: Automated analysis of protein subcellular location in time series images Bioinformatics, 2010; 26 (13): 1630-1636
- C. Jackson, R. F. Murphy, and J. Kovacevic: Intelligent Acquisition and Learning of Fluorescence Microscope Data Models, IEEE Trans. Image Proc. 2009; 18 (9): 2071-2084
- Y. Qian and R.F. Murphy: Improved Recognition of Figures containing Fluorescence Microscope Images in Online Journal Articles using Graphical Models. Bioinformatics, 2008; 24:569-576.
- G. K. Rohde, A. Ribeiro, K. N. Dahl, and R. F. Murphy: Deformation-based nuclear morphometry: capturing nuclear shape variation in HeLa Cells. Cytometry, 2008; 73A:341-350.
- S.-C. Chen, G. J. Gordon, and R.F. Murphy: Graphical Models for Structured Classification, with an Application to Interpreting Images of Protein Subcellular Location Patterns. J. Machine Learning Res., 2008; 9:651-682.
- J. Newberg and R.F. Murphy: A Framework for the Automated Analysis of Subcellular Patterns in Human Protein Atlas Images. J. Proteome Res., 2008; 7: 2300-2308.
- E. Glory and R.F. Murphy:. Automated Subcellular Location Determination and High Throughput Microscopy. Developmental Cell, 2007; 12:7-16.
- S.-C. Chen, T. Zhao, G. J. Gordon, and R. F. Murphy: Automated Image Analysis of Protein Localization in Budding Yeast. Bioinformatics, 2007; 23:i66-i71.
- T. Zhao and R.F. Murphy: Automated Learning of Generative Models for Subcellular Location: Building Blocks for Systems Biology. Cytometry, 2007; 71A:978-990.
- T. Zhao, M. Velliste, M.V. Boland, and R.F. Murphy: Object Type Recognition for Automated Analysis of Protein Subcellular Location. IEEE Trans. Image Proc., 2005; 14:1351-1359.
Automated interpretation of fluorescence microscope images
Prof. Murphy´s laboratory combines research in cell and computational biology. He has developed tools for objectively choosing a representative microscopic image from a set, tools for comparing sets of images, systems for automating the determination of subcellular location, and tools for organizing unknown proteins by their location patterns. The critical component of each of the systems is a set of numerical features that capture essential biological information in the images. The work has implications for automated characterization of newly identified proteins and for high-throughput drug screening using microscopy.
Dr. Murphy's work 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. He is especially interested in modeling of spatiotemporal subcellular patterns and application of active learning methods to biological problems.