Robert F. Murphy -Department of Biological Sciences - Carnegie Mellon University

Robert F. Murphy

Professor of Biological Sciences, Biomedical Engineering, Machine Learning

Head, Computational Biology Department

7723 Gates/Hillman Center
Department of Biological Sciences
Carnegie Mellon University
Pittsburgh, PA 15213
Phone: 412-268-3480
Fax: 412-268-7129


Ph.D., California Institute of Technology
Postdoctoral Appointment, Columbia University


My group's work combines my interests in cell and computational biology. We apply both experimental and computational methods to the fundamental problem of learning and representing how proteins are organized within eukaryotic cells. For this we particularly use automated microscopy combined with methods from machine learning, pattern recognition and modeling. Much of our recent work focuses on automated learning of generative models of subcellular organization that have the promise to allow information from diverse methodologies to be combined to compactly represent current knowledge and enable predictions about how organization changes during development and disease. A second major focus is on intelligent sampling in very large dimensional experimental spaces, such as in the context of learning the effect of thousands of potential drugs on thousands of potential targets.


Temerinac-Ott M, Naik AW, Murphy RF. Deciding when to stop: efficient experimentation to learn to predict drug-target interactions. BMC Bioinformatics. 2015 Jul 9;16:213

Figge MT, Murphy RF. Image-based systems biology. Cytometry A. 2015 Jun;87(6):459-61.

Murphy RF. A new era in bioimage informatics. Bioinformatics. 2014 May 15;30(10):1353

Naik AW, Kangas JD, Langmead CJ, Murphy RF (2013) Efficient Modeling and Active Learning Discovery of Biological Responses. PLoS ONE 8(12): e83996. doi:10.1371/journal.pone.0083996

Buck TE, Li J, Rohde GK, Murphy RF. Toward the virtual cell: automated approaches to building models of subcellular organization "learned" from microscopy images. Bioessays. 2012 Sep;34(9):791-9.

Lin T, Bar-Joseph Z, and Murphy RF. Learning Cellular Sorting Pathways Using Protein Interactions and Sequence Motifs. Journal of Computational Biology, 2011, in press.

Jackson C, Glory E, Murphy RF, and Kovacevic J. Model building and intelligent acquisition with application to protein subcellular location classification. Bioinformatics 27:1854-1859, 2011.

Murphy RF. An active role for machine learning in drug development. Nature Chemical Biology 7:327-330, 2011.

Peng T and Murphy RF. Image-derived, Three-dimensional Generative Models of Cellular Organization. Cytometry Part A 79A:383-391, 2011.

Lin T, Murphy RF, Bar-Joseph Z. Discriminative Motif Finding for Predicting Protein Subcellular Localization. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2010, in press.

Peng T, Bonamy GMC, Glory-Afshar E, Rines DR, Chanda SK, Murphy RF. Determining the distribution of probes between different subcellular locations through automated unmixing of subcellular patterns. Proc. Natl. Acad. Sci. U.S.A. 107:2944-2949, 2010.

Coelho LP, Peng T, Murphy RF. Quantifying the distribution of probes between subcellular locations using unsupervised pattern unmixing. Bioinformatics 26:i7-i12, 2010.

Shariff A, Rohde GK, Murphy RF. A Generative Model of Microtubule Distributions, and Indirect Estimation of its Parameters from Fluorescence Microscopy Images. Cytometry 77A:457-466, 2010.

Murphy RF. Communicating Subcellular Distributions. Cytometry Part A 77A:686-692, 2010.

Hu Y, Garcia Osuna E, Hua J, Nowicki TS, Stolz R, McKayle C, Murphy RF. Automated Analysis of Protein Subcellular Locations in Time Series Images. Bioinformatics 26:1630-1636, 2010.

Newberg J, Murphy RF. A Framework for the Automated Analysis of Subcellular Patterns in Human Protein Atlas Images. J. Proteome Res. 7: 2300-2308, 2008.

Rohde GK, Ribeiro A, Dahl KN, Murphy RF. Deformation-based nuclear morphometry: capturing nuclear shape variation in HeLa Cells. Cytometry, 73A:341-350, 2008.

Chen S-C, Gordon GJ, Murphy RF. Graphical Models for Structured Classification, with an Application to Interpreting Images of Protein Subcellular Location Patterns. J. Machine Learning Res. 9:651-682, 2008.

Zhao T, Murphy RF. Automated Learning of Generative Models for Subcellular Location: Building Blocks for Systems Biology. Cytometry 71A:978-990, 2007.

Boland MV, Murphy RF. A Neural Network Classifier Capable of Recognizing the Patterns of all Major Subcellular Structures in Fluorescence Microscope Images of HeLa Cells. Bioinformatics 17:1213-1223, 2001.

Full PubMed Listnings

Virtual Tour

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