Selected Publications
- 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.
FRIAS Project
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.