Research Topics
| R F MurphySummaryAffiliation: Carnegie Mellon University Country: USA Publications
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Publications
Advances in molecular labeling, high throughput imaging and machine intelligence portend powerful functional cellular biochemistry toolsJeffrey 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...
From quantitative microscopy to automated image understandingKai 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....- Location proteomics: a systems approach to subcellular locationR 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... - Automated interpretation of protein subcellular location patternsXiang Chen
Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
Int Rev Cytol 249:193-227. 2006.... - Boosting accuracy of automated classification of fluorescence microscope images for location proteomicsKai 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... - A graphical model approach to automated classification of protein subcellular location patterns in multi-cell imagesShann-Ching Chen
Department of Biomedical Engineering and Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA 15213, USA
BMC Bioinformatics 7:90. 2006..We also anticipate that it will be useful for analyzing the mixtures of cell types typically present in images of tissues. Lastly, we anticipate that the method can be generalized to other problems... - A multiresolution approach to automated classification of protein subcellular location imagesAmina 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... - Cytomics and location proteomics: automated interpretation of subcellular patterns in fluorescence microscope imagesRobert 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 - Towards a systematics for protein subcelluar location: quantitative description of protein localization patterns and automated analysis of fluorescence microscope imagesR 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... - Automated interpretation of protein subcellular location patterns: implications for early cancer detection and assessmentRobert 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... - Communicating subcellular distributionsRobert 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.... 
A framework for the automated analysis of subcellular patterns in human protein atlas imagesJustin 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...- A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cellsM 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.. - Model building and intelligent acquisition with application to protein subcellular location classificationC 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... - Efficient framework for automated classification of subcellular patterns in budding yeastSeungil Huh
Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
Cytometry A 75:934-40. 2009.... - Automated learning of generative models for subcellular location: building blocks for systems biologyTing 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... - Location proteomics: systematic determination of protein subcellular locationJustin Newberg
Department of Biomedical Engineering and Center for Bioimage Informatics, Carnegie Mellon University, Pittsburg, PA, USA
Methods Mol Biol 500:313-32. 2009.... - Improved recognition of figures containing fluorescence microscope images in online journal articles using graphical modelsYuntao 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... - Deformation-based nuclear morphometry: capturing nuclear shape variation in HeLa cellsGustavo 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... - Object type recognition for automated analysis of protein subcellular locationTing Zhao
Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
IEEE Trans Image Process 14:1351-9. 2005.... - High-recall protein entity recognition using a dictionaryZhenzhen Kou
Center for Automated Learning and Discovery, Carnegie Mellon University Pittsburgh, PA 15213, USA
Bioinformatics 21:i266-73. 2005..AVAILABILITY: Dictionary HMMs were implemented in Java. Algorithms are available through an information extraction package MINORTHIRD on http://minorthird.sourceforge.net.. - Determining the distribution of probes between different subcellular locations through automated unmixing of subcellular patternsTao 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... - Discriminative motif finding for predicting protein subcellular localizationTien 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/... - Automated, systematic determination of protein subcellular location using fluorescence microscopyElvira 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... - Mitotic Golgi is in a dynamic equilibrium between clustered and free vesicles independent of the ERS A Jesch
Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Traffic 2:873-84. 2001.... - Cell cycle dependence of protein subcellular location inferred from static, asynchronous imagesTaraz 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... - A generative model of microtubule distributions, and indirect estimation of its parameters from fluorescence microscopy imagesAabid 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... - Quantifying the distribution of probes between subcellular locations using unsupervised pattern unmixingLuis Pedro Coelho
Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Bioinformatics 26:i7-12. 2010.... - Automated image analysis of protein localization in budding yeastShann 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... - Large-scale automated analysis of location patterns in randomly tagged 3T3 cellsElvira 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... - Intelligent acquisition and learning of fluorescence microscope data modelsCharles 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... - Objective evaluation of differences in protein subcellular distributionEdward 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... - Automated interpretation of subcellular patterns in fluorescence microscope images for location proteomicsXiang 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... - Automated subcellular location determination and high-throughput microscopyEstelle 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 - Automated analysis of protein subcellular location in time series imagesYanhua 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... - Multivariate analysisM 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... - Dispersal of Golgi matrix proteins during mitotic Golgi disassemblySapna 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... - Detection of protein-protein interactions through vesicle targetingJacob 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... - Automated interpretation of subcellular patterns from immunofluorescence microscopyYanhua 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... - Automated image analysis for high-content screening and analysisAabid 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... - Characterization of the TGN exit signal of the human mannose 6-phosphate uncovering enzymePrashant 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... - Putting proteins on the mapRobert F Murphy
Nat Biotechnol 24:1223-4. 2006 - Opening of size-selective pores in endosomes during human rhinovirus serotype 2 in vivo uncoating monitored by single-organelle flow analysisMarianne 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... - Transferrin recycling and dextran transport to lysosomes is differentially affected by bafilomycin, nocodazole, and low temperatureGünther 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... - From imaging to understanding: Frontiers in Live Cell Imaging, Bethesda, MD, April 19-21, 2006Yu-li Wang
Department of Physiology, University of Massachusetts Medical School, Worcester, 01655, USA
J Cell Biol 174:481-4. 2006
Research Grants
- ROBUST PORTABLE SOFTWARE FOR LOCATION PROTEOMICSRobert Murphy; Fiscal Year: 2006....
- Probabilistic Modeling of Information from Images and Text in Online JournalsRobert Murphy; Fiscal Year: 2007....
- Building and Validating Location Proteomics DatabasesRobert 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. ..
- Building and Validating Location Proteomics DatabasesRobert Murphy; Fiscal Year: 2009..The work has the potential to dramatically change the way cell-based assays are used in drug discovery. ..
- Building and Validating Location Proteomics DatabasesRobert 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. ..