R F Murphy

Summary

Affiliation: Carnegie Mellon University
Country: USA

Publications

  1. ncbi request reprint Advances in molecular labeling, high throughput imaging and machine intelligence portend powerful functional cellular biochemistry tools
    Jeffrey H Price
    Department of Bioengineering, University of California San Diego, La Jolla, California, USA
    J Cell Biochem Suppl 39:194-210. 2002
  2. pmc From quantitative microscopy to automated image understanding
    Kai Huang
    Carnegie Mellon University, Center for Automated Learning and Discovery, Departments of Biological Sciences and Biomedical Engineering, 4400 Fifth Avenue, Pittsburgh, Pennsylvania 15213, USA
    J Biomed Opt 9:893-912. 2004
  3. ncbi request reprint Location proteomics: a systems approach to subcellular location
    R F Murphy
    Department of Biological Sciences, Center for Automated Learning and Discovery and Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Biochem Soc Trans 33:535-8. 2005
  4. ncbi request reprint Automated interpretation of protein subcellular location patterns
    Xiang Chen
    Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
    Int Rev Cytol 249:193-227. 2006
  5. ncbi request reprint Efficient discovery of responses of proteins to compounds using active learning
    Joshua D Kangas
    Lane Center for Computational Biology, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA
    BMC Bioinformatics 15:143. 2014
  6. pmc Quantifying the distribution of probes between subcellular locations using unsupervised pattern unmixing
    Luis Pedro Coelho
    Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Bioinformatics 26:i7-12. 2010
  7. pmc Efficient modeling and active learning discovery of biological responses
    Armaghan W Naik
    Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
    PLoS ONE 8:e83996. 2013
  8. pmc Automated analysis and reannotation of subcellular locations in confocal images from the Human Protein Atlas
    Jieyue Li
    Center for Bioimage Informatics and Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
    PLoS ONE 7:e50514. 2012
  9. pmc Boosting accuracy of automated classification of fluorescence microscope images for location proteomics
    Kai Huang
    Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213 USA
    BMC Bioinformatics 5:78. 2004
  10. pmc A graphical model approach to automated classification of protein subcellular location patterns in multi-cell images
    Shann Ching Chen
    Department of Biomedical Engineering and Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    BMC Bioinformatics 7:90. 2006

Collaborators

Detail Information

Publications49

  1. ncbi request reprint Advances in molecular labeling, high throughput imaging and machine intelligence portend powerful functional cellular biochemistry tools
    Jeffrey H Price
    Department of Bioengineering, University of California San Diego, La Jolla, California, USA
    J Cell Biochem Suppl 39:194-210. 2002
    ..The goal of finally understanding all cellular components and behaviors will be achieved by advances in both instrumentation engineering (software and hardware) and molecular biochemistry...
  2. pmc From quantitative microscopy to automated image understanding
    Kai Huang
    Carnegie Mellon University, Center for Automated Learning and Discovery, Departments of Biological Sciences and Biomedical Engineering, 4400 Fifth Avenue, Pittsburgh, Pennsylvania 15213, USA
    J Biomed Opt 9:893-912. 2004
    ....
  3. ncbi request reprint Location proteomics: a systems approach to subcellular location
    R F Murphy
    Department of Biological Sciences, Center for Automated Learning and Discovery and Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Biochem Soc Trans 33:535-8. 2005
    ..Preliminary work suggests the feasibility of expressing each unique pattern as a generative model that can be incorporated into comprehensive models of cell behaviour...
  4. ncbi request reprint Automated interpretation of protein subcellular location patterns
    Xiang Chen
    Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
    Int Rev Cytol 249:193-227. 2006
    ....
  5. ncbi request reprint Efficient discovery of responses of proteins to compounds using active learning
    Joshua D Kangas
    Lane Center for Computational Biology, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA
    BMC Bioinformatics 15:143. 2014
    ..An alternative approach would be to screen against undesired effects early in the process, but the number of possible secondary targets makes this prohibitively expensive...
  6. pmc Quantifying the distribution of probes between subcellular locations using unsupervised pattern unmixing
    Luis Pedro Coelho
    Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Bioinformatics 26:i7-12. 2010
    ....
  7. pmc Efficient modeling and active learning discovery of biological responses
    Armaghan W Naik
    Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
    PLoS ONE 8:e83996. 2013
    ..Using simulated and experimental data, we show that our approaches can produce powerful predictive models without exhaustive experimentation and can learn them much faster than by selecting experiments at random. ..
  8. pmc Automated analysis and reannotation of subcellular locations in confocal images from the Human Protein Atlas
    Jieyue Li
    Center for Bioimage Informatics and Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
    PLoS ONE 7:e50514. 2012
    ..These proteins were reexamined by the original annotators, and a significant fraction had their annotations changed. The results demonstrate that automated approaches can provide an important complement to visual annotation...
  9. pmc Boosting accuracy of automated classification of fluorescence microscope images for location proteomics
    Kai Huang
    Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213 USA
    BMC Bioinformatics 5:78. 2004
    ..Building on these results, we evaluate here new classifiers and features to improve the recognition of protein subcellular location patterns in both 2D and 3D fluorescence microscope images...
  10. pmc A graphical model approach to automated classification of protein subcellular location patterns in multi-cell images
    Shann Ching Chen
    Department of Biomedical Engineering and Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    BMC Bioinformatics 7:90. 2006
    ..Since cells displaying the same location pattern are often clustered together, considering multiple cells may be expected to improve discrimination between similar patterns...
  11. pmc A multiresolution approach to automated classification of protein subcellular location images
    Amina Chebira
    Center for Bioimage Informatics and Dept of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
    BMC Bioinformatics 8:210. 2007
    ..5%, was obtained by including a set of multiresolution features. This demonstrates the value of multiresolution approaches to this important problem...
  12. ncbi request reprint Towards a systematics for protein subcelluar location: quantitative description of protein localization patterns and automated analysis of fluorescence microscope images
    R F Murphy
    Department of Biological Sciences and Center for Light Microscope Imaging and Biotechnology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Proc Int Conf Intell Syst Mol Biol 8:251-9. 2000
    ..A key conclusion is that, at least in certain cases, these automated approaches are better able to distinguish similar protein localization patterns than human observers...
  13. ncbi request reprint Cytomics and location proteomics: automated interpretation of subcellular patterns in fluorescence microscope images
    Robert F Murphy
    Department of Biological Sciences, Center for Automated Learning and Discovery, and Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
    Cytometry A 67:1-3. 2005
  14. pmc Communicating subcellular distributions
    Robert F Murphy
    Lane Center for Computational Biology and Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
    Cytometry A 77:686-92. 2010
    ....
  15. ncbi request reprint Automated interpretation of protein subcellular location patterns: implications for early cancer detection and assessment
    Robert F Murphy
    Department of Biological Sciences, and Center for Automated Learning and Discovery, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, PA 15213, USA
    Ann N Y Acad Sci 1020:124-31. 2004
    ..The possible use of automated pattern analysis methods for improving detection of abnormal cells in cancerous or precancerous tissues is also discussed...
  16. doi request reprint A framework for the automated analysis of subcellular patterns in human protein atlas images
    Justin Newberg
    Center for Bioimage Informatics, and Departments of Biological Sciences, Biomedical Engineering, and Machine Learning, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15217, USA
    J Proteome Res 7:2300-8. 2008
    ..The approach described is an important starting point for automatically assigning subcellular locations on a proteome-wide basis for collections of tissue images such as the Atlas...
  17. ncbi request reprint A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells
    M V Boland
    Center for Light Microscope Imaging and Biotechnology, Biomedical and Health Engineering Program, Carnegie Mellon University, 4400 Fifth Ave, Pittsburgh, PA 15213, USA
    Bioinformatics 17:1213-23. 2001
    ..The scripts and source code generated for this work are available at http://murphylab.web.cmu.edu/software. CONTACT: murphy@cmu.edu..
  18. ncbi request reprint Automated learning of generative models for subcellular location: building blocks for systems biology
    Ting Zhao
    Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
    Cytometry A 71:978-90. 2007
    ..They can potentially be combined for many proteins to yield a high resolution location map in support of systems biology...
  19. pmc Efficient framework for automated classification of subcellular patterns in budding yeast
    Seungil Huh
    Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
    Cytometry A 75:934-40. 2009
    ....
  20. pmc Discriminative motif finding for predicting protein subcellular localization
    Tien ho Lin
    Carnegie Mellon University, Pittsburgh, Pittsburgh, PA 15213, USA
    IEEE/ACM Trans Comput Biol Bioinform 8:441-51. 2011
    ..A software implementation and the data set described in this paper are available from http://murphylab.web.cmu.edu/software/2009_TCBB_motif/...
  21. doi request reprint Location proteomics: systematic determination of protein subcellular location
    Justin Newberg
    Department of Biomedical Engineering and Center for Bioimage Informatics, Carnegie Mellon University, Pittsburg, PA, USA
    Methods Mol Biol 500:313-32. 2009
    ....
  22. pmc Improved recognition of figures containing fluorescence microscope images in online journal articles using graphical models
    Yuntao Qian
    Center for Bioimage Informatics and Machine Learning Department, Carnegie Mellon University, Pittsburgh, USA
    Bioinformatics 24:569-76. 2008
    ..However, the types of panels in a figure are often correlated, so that we can consider the class of a panel to be dependent not only on its own features but also on the types of the other panels in a figure...
  23. pmc High-recall protein entity recognition using a dictionary
    Zhenzhen Kou
    Center for Automated Learning and Discovery, Carnegie Mellon University Pittsburgh, PA 15213, USA
    Bioinformatics 21:i266-73. 2005
    ....
  24. ncbi request reprint Automated, systematic determination of protein subcellular location using fluorescence microscopy
    Elvira García Osuna
    Center for Bioimage Informatics, Department of Biomedical Engineering, Carnegie Mellon University Pittsburgh, PA, USA
    Subcell Biochem 43:263-76. 2007
    ..This chapter reviews this work and describes current efforts to extend these approaches, including classification of temporal patterns and building of generative models to represent location patterns...
  25. pmc Determining the distribution of probes between different subcellular locations through automated unmixing of subcellular patterns
    Tao Peng
    Center for Bioimage Informatics and Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Proc Natl Acad Sci U S A 107:2944-9. 2010
    ..The method enables automated and unbiased determination of the distributions of protein across cellular compartments, and will significantly improve imaging-based high-throughput assays and facilitate proteome-scale localization efforts...
  26. pmc Object type recognition for automated analysis of protein subcellular location
    Ting Zhao
    Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    IEEE Trans Image Process 14:1351-9. 2005
    ....
  27. doi request reprint Deformation-based nuclear morphometry: capturing nuclear shape variation in HeLa cells
    Gustavo K Rohde
    Center for Bioimage Informatics and Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
    Cytometry A 73:341-50. 2008
    ..Results obtained by analyzing two sets of images of HeLa cells are shown. In addition to identifying the modes of variation in normal HeLa nuclei, the effects of lamin A/C on nuclear morphology are quantitatively described...
  28. pmc Model building and intelligent acquisition with application to protein subcellular location classification
    C Jackson
    Center for Bioimage Informatics, Department of Biomedical Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA
    Bioinformatics 27:1854-9. 2011
    ..Our key innovation is to build models during acquisition rather than as a post-processing step, thus allowing us to intelligently and automatically adapt the acquisition process given the model acquired...
  29. doi request reprint Intelligent acquisition and learning of fluorescence microscope data models
    Charles Jackson
    Department of Biomedical Engineering and the Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    IEEE Trans Image Process 18:2071-84. 2009
    ..Results, both on synthetic as well as real data, demonstrate accurate model building and large efficiency gains during acquisition...
  30. pmc A generative model of microtubule distributions, and indirect estimation of its parameters from fluorescence microscopy images
    Aabid Shariff
    Lane Center for Computational Biology and Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
    Cytometry A 77:457-66. 2010
    ..c) 2010 International Society for Advancement of Cytometry...
  31. ncbi request reprint Objective evaluation of differences in protein subcellular distribution
    Edward J S Roques
    Department of Biological Sciences and Center for Light Microscope Imaging and Biotechnology, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh PA 15213, USA
    Traffic 3:61-5. 2002
    ..This approach provides a high throughput and reproducible technique to determine whether image distributions differ within a specified statistical confidence, and is shown to resolve image sets indistinguishable by visual inspection...
  32. pmc Cell cycle dependence of protein subcellular location inferred from static, asynchronous images
    Taraz E Buck
    Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Conf Proc IEEE Eng Med Biol Soc 2009:1016-9. 2009
    ..We additionally show that a one-dimensional parameterization of cell cycle progression and protein feature pattern is sufficient to infer association between localization and cell cycle...
  33. pmc Large-scale automated analysis of location patterns in randomly tagged 3T3 cells
    Elvira García Osuna
    Center for Bioimage Informatics, HHC119, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Ann Biomed Eng 35:1081-7. 2007
    ..This approach represents a powerful automated solution to the problem of identifying subcellular locations on a proteome-wide basis for many different cell types...
  34. ncbi request reprint Automated image analysis of protein localization in budding yeast
    Shann Ching Chen
    Center for Bioimage Informatics, Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Bioinformatics 23:i66-71. 2007
    ..Based on our past success at building automated systems to classify subcellular location patterns in mammalian cells, we sought to create a similar system for yeast...
  35. pmc Automated interpretation of subcellular patterns in fluorescence microscope images for location proteomics
    Xiang Chen
    Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Cytometry A 69:631-40. 2006
    ..Second, it will provide tools for Cytomics projects aimed at characterizing the behaviors of all cell types before, during, and after the onset of various diseases...
  36. ncbi request reprint Mitotic Golgi is in a dynamic equilibrium between clustered and free vesicles independent of the ER
    S A Jesch
    Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Traffic 2:873-84. 2001
    ....
  37. pmc Automated analysis of protein subcellular location in time series images
    Yanhua Hu
    Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Bioinformatics 26:1630-6. 2010
    ..The goal is to use temporal features to improve recognition of protein patterns that are not fully distinguishable by their static features alone...
  38. ncbi request reprint Automated subcellular location determination and high-throughput microscopy
    Estelle Glory
    Center for Bioimage Informatics, Molecular Biosensor and Imaging Center, and Department of Biological Sciences, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, PA 15213, USA
    Dev Cell 12:7-16. 2007
  39. ncbi request reprint Multivariate analysis
    M V Boland
    Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
    Curr Protoc Cytom . 2001
    ..Keywords: principal components analysis; cluster analysis; FCS file format One of the goals of Current Protocols is to provide information from the very basic level to the very advanced...
  40. ncbi request reprint Automated interpretation of subcellular patterns from immunofluorescence microscopy
    Yanhua Hu
    Department of Biological Sciences, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, PA 15213, USA
    J Immunol Methods 290:93-105. 2004
    ..The programs described provide an important set of tools for those using fluorescence microscopy to study protein location...
  41. ncbi request reprint Dispersal of Golgi matrix proteins during mitotic Golgi disassembly
    Sapna Puri
    Department of Biological Sciences, Carnegie Mellon University, 4400 5th Avenue, Pittsburgh, PA 15213, USA
    J Cell Sci 117:451-6. 2004
    ..The extensive disassembly of matrix proteins argues against their participation in a stable template and supports a self-assembly mode of Golgi biogenesis...
  42. doi request reprint Automated image analysis for high-content screening and analysis
    Aabid Shariff
    Lane Center for Computational Biology and Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA, USA
    J Biomol Screen 15:726-34. 2010
    ..An overview of these image analysis tools is presented here, along with brief descriptions of a few applications...
  43. pmc Detection of protein-protein interactions through vesicle targeting
    Jacob H Boysen
    Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
    Genetics 182:33-9. 2009
    ..cerevisiae and the fungal pathogen Candida albicans. We use computational analysis of microscopic images to provide a quantitative and automated assessment of confidence...
  44. ncbi request reprint Characterization of the TGN exit signal of the human mannose 6-phosphate uncovering enzyme
    Prashant Nair
    Institute of Physiology, University of Zurich, Zurich, 8057, Switzerland
    J Cell Sci 118:2949-56. 2005
    ..The identification of a trans-Golgi network exit signal in its cytoplasmic tail elucidates the trafficking pathway of uncovering enzyme, a crucial player in the process of lysosomal biogenesis...
  45. ncbi request reprint Putting proteins on the map
    Robert F Murphy
    Nat Biotechnol 24:1223-4. 2006
  46. pmc Opening of size-selective pores in endosomes during human rhinovirus serotype 2 in vivo uncoating monitored by single-organelle flow analysis
    Marianne Brabec
    Department of Pathophysiology, Center for Physiology and Pathophysiology, Medical University of Vienna, Waehringer Guertel 18 20, A 1090 Vienna, Austria
    J Virol 79:1008-16. 2005
    ..This finding is in keeping with the low-pH requirement of HRV2 infection; for adenovirus, no pH dependence for endosomal escape was found with this drug...
  47. pmc From imaging to understanding: Frontiers in Live Cell Imaging, Bethesda, MD, April 19-21, 2006
    Yu li Wang
    Department of Physiology, University of Massachusetts Medical School, Worcester, 01655, USA
    J Cell Biol 174:481-4. 2006
    ..The goal was to turn live cell imaging from a "technique" used in cell biology into a new exploratory science that combines a number of research fields...
  48. ncbi request reprint Transferrin recycling and dextran transport to lysosomes is differentially affected by bafilomycin, nocodazole, and low temperature
    Günther Baravalle
    Department of Pathophysiology, Center for Physiology and Pathophysiology, Medical University of Vienna, Wahringer Gurtel 18 20, 1090, Vienna, Austria
    Cell Tissue Res 320:99-113. 2005
    ..Consequently, these treatments can be applied to investigate whether internalized macromolecules such as viruses follow a recycling or degradative pathway...

Research Grants13

  1. ROBUST PORTABLE SOFTWARE FOR LOCATION PROTEOMICS
    Robert Murphy; Fiscal Year: 2006
    ....
  2. Probabilistic Modeling of Information from Images and Text in Online Journals
    Robert Murphy; Fiscal Year: 2007
    ....
  3. Building and Validating Location Proteomics Databases
    Robert Murphy; Fiscal Year: 2007
    ..The ability to synthesize distributions will provide an important structural framework for systems biology modeling of cell behavior in normal and disease states. ..
  4. Building and Validating Location Proteomics Databases
    Robert F Murphy; Fiscal Year: 2010
    ..The ability to synthesize distributions will provide an important structural framework for systems biology modeling of cell behavior in normal and disease states. ..
  5. ROBUST PORTABLE SOFTWARE FOR LOCATION PROTEOMICS
    Robert Murphy; Fiscal Year: 2005
    ....
  6. Probabilistic Modeling of Information from Images/Text
    Robert Murphy; Fiscal Year: 2006
    ....
  7. Building and Validating Location Proteomics Databases
    Robert Murphy; Fiscal Year: 2009
    ..The ability to synthesize distributions will provide an important structural framework for systems biology modeling of cell behavior in normal and disease states. ..
  8. Building and Validating Location Proteomics Databases
    Robert Murphy; Fiscal Year: 2009
    ..The work has the potential to dramatically change the way cell-based assays are used in drug discovery. ..
  9. ROBUST PORTABLE SOFTWARE FOR LOCATION PROTEOMICS
    Robert Murphy; Fiscal Year: 2003
    ....
  10. ROBUST PORTABLE SOFTWARE FOR LOCATION PROTEOMICS
    Robert Murphy; Fiscal Year: 2004
    ....
  11. Building and Validating Location Proteomics Databases
    Robert F Murphy; Fiscal Year: 2010
    ..The ability to synthesize distributions will provide an important structural framework for systems biology modeling of cell behavior in normal and disease states. ..