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Determining the subcellular location of new proteins from microscope images using local features

Luis Pedro Coelho, Joshua D. Kangas, Armaghan W. Naik, Elvira Osuna-Highley, Estelle Glory-Afshar, Margaret Fuhrman, Ramanuja Simha, Peter B. Berget, Jonathan W. Jarvik, Robert F. Murphy
2013 Computer applications in the biosciences : CABIOS  
Motivation: Evaluation of previous systems for automated determination of subcellular location from microscope images has been done using datasets in which each location class consisted of multiple images  ...  These extended the notion of local features to exploit both the protein image and any reference markers that were imaged in parallel.  ...  ACKNOWLEDGEMENTS We thank the HPA project team, especially Emma Lundberg, for providing the high-resolution confocal microscopy images used for the HPA dataset, and Jieyue Li for preparing this dataset  ... 
doi:10.1093/bioinformatics/btt392 pmid:23836142 pmcid:PMC3753569 fatcat:e3v6kzjiobeuhnsdbdkgxyv77m

Automated Subcellular Location Determination and High-Throughput Microscopy

Estelle Glory, Robert F. Murphy
2007 Developmental Cell  
The second step determines the subcellular location class of a target protein from its fluorescent microscope images. The selected features are computed and used as inputs of the trained classifier.  ...  The input is the set of subcellular location features extracted from a fluorescent microscope image (see Figure 2) .  ... 
doi:10.1016/j.devcel.2006.12.007 pmid:17199037 fatcat:xklrrwtxazg67awsyvtrlx4354

Towards a systematics for protein subcelluar location: quantitative description of protein localization patterns and automated analysis of fluorescence microscope images

R F Murphy, M V Boland, M Velliste
2000 Proceedings. International Conference on Intelligent Systems for Molecular Biology  
Since subcellular location plays a crucial role in protein function, the availability of systems that can predict location from sequence or high-throughput systems that determine location experimentally  ...  This paper describes an approach to the quantitative description of protein localization patterns using numerical features and the use of these features to develop classifiers that can recognize all major  ...  A major limitation of the usefulness of these systems is that only broad categories of subcellular locations were used (see Table 1 ).  ... 
pmid:10977086 fatcat:dbkgqamuyfch7bsbcuq4rttkeu

Location Proteomics: Systematic Determination of Protein Subcellular Location [chapter]

Justin Newberg, Juchang Hua, Robert F. Murphy
2009 Msphere  
The feasibility of objectively grouping proteins into subcellular location families, and in the process of discovering new subcellular patterns, has been demonstrated using cluster analysis of images from  ...  Automated and objective methods for determining protein subcellular location have been described based on extracting numerical features from fluorescence microscope images and applying machine learning  ...  The high intensity blobs of pixels in fluorescence microscope images might be the first thing Interpretation of Protein Subcellular Location Images Subcellular Location Features SLFs for 2D Images  ... 
doi:10.1007/978-1-59745-525-1_11 pmid:19399439 fatcat:eljo3w6chbh5ppdymrjc5xvrai

Robust Numerical Features for Description and Classification of Subcellular Location Patterns in Fluorescence Microscope Images

Robert F. Murphy, Meel Velliste, Gregory Porreca
2003 Journal of VLSI Signal Processing Systems for Signal, Image and Video Technology  
As large amounts of data become available from genome-wide determination of protein subcellular location, automated approaches to categorizing and comparing location patterns are urgently needed.  ...  Since subcellular location is most often determined using fluorescence microscopy, we have developed automated systems for interpreting the resulting images.  ...  Acknowledgments This work was supported in part by NIH grant R33 CA83219 and by a research grant from the Commonwealth of Pennsylvania Tobacco Settlement Fund.  ... 
doi:10.1023/b:vlsi.0000003028.71666.44 fatcat:chxrre6yvjfidaq4jy5dk5pz34

From quantitative microscopy to automated image understanding

Kai Huang, Robert F. Murphy
2004 Journal of Biomedical Optics  
Fig. 11 A SLT created by using the best ten features selected from SLF11 by SDA for the 46 proteins from the 3T3 image collection. 5 From quantitative microscopy .  ...  We describe a systematic approach for interpreting protein subcellular distributions using various sets of subcellular location features (SLF), in combination with supervised classification and unsupervised  ...  Our methods, along with high-throughput imaging hardware, can be used to determine the subcellular location of every protein expressed in a certain cell type, which results in a complete location tree  ... 
doi:10.1117/1.1779233 pmid:15447010 pmcid:PMC1458526 fatcat:b3fzgpl2lzh2hepqd76lrh66vy

Location proteomics: building subcellular location trees from high-resolution 3D fluorescence microscope images of randomly tagged proteins

Xiang Chen, Meel Velliste, Shmuel Weinstein, Jonathan W. Jarvik, Robert F. Murphy, Dan V. Nicolau, Joerg Enderlein, Robert C. Leif, Daniel L. Farkas
2003 Manipulation and Analysis of Biomolecules, Cells, and Tissues  
We describe here the application of the numerical features from the previous work to images obtained by random tagging of proteins.  ...  A set of 42 numerical features describing both image texture and object morphology were calculated and used to build subcellular location trees that group the tagged proteins by similarity of location  ...  ACKNOWLEDGMENTS This work was supported in part by NIH grant R33 CA83219 and by a research grant from the Commonwealth of Pennsylvania Tobacco Settlement Fund.  ... 
doi:10.1117/12.477899 fatcat:xont5t4btndpbnqriftwkmugm4

Automated interpretation of subcellular patterns in fluorescence microscope images for location proteomics

Xiang Chen, Meel Velliste, Robert F. Murphy
2006 Cytometry Part A  
Currently, subcellular location patterns are routinely determined by visual inspection of fluorescence microscope images.  ...  These employ numerical feature extraction from images, feature reduction to identify the most useful features, and various supervised learning (classification) and unsupervised learning (clustering) methods  ...  A decade ago, we set as our initial task to test whether systematic analysis of protein subcellular location was feasible using numerical features computed from fluorescence microscope images (2).  ... 
doi:10.1002/cyto.a.20280 pmid:16752421 pmcid:PMC2901544 fatcat:b4j3pcgfybcnzky2anhqnpojgy

Large-Scale Automated Analysis of Location Patterns in Randomly Tagged 3T3 Cells

Elvira García Osuna, Juchang Hua, Nicholas W. Bateman, Ting Zhao, Peter B. Berget, Robert F. Murphy
2007 Annals of Biomedical Engineering  
Location proteomics is concerned with the systematic analysis of the subcellular location of proteins.  ...  Here we describe the use of such methods on a large collection of images obtained by automated microscopy to perform high-throughput analysis of endogenous proteins randomly-tagged with a fluorescent protein  ...  This work was supported by Commonwealth of Pennsylvania Tobacco Settlement Fund grant 017393, NIH grant GM068845-01, and NSF grant EF-0331657.  ... 
doi:10.1007/s10439-007-9254-5 pmid:17285363 pmcid:PMC2901537 fatcat:2wivxryhh5ebbmg6t45tdtrwde

Automated Classification of Subcellular Localization of Proteins in HeLa Cells Using Fluorescent Microscopic Images

Noriko Kabuyama, Reiko Minamikawa-Tachino Reiko Minamikawa-Tachino, Toshiyuki Gotoh, Seiichiro Kagei, Takushi Togashi, Sumio Sugano, Takahiro Tsunekawa, Nobuo Nomura
2004 Chem-Bio Informatics Journal  
We have developed a system to classify cellular forms of fusion proteins with an enhanced yellow fluorescent protein (EYFP) into subcellular compartments using images by a fluorescent microscope.  ...  The patterns are extended models of the cellular forms which are the result of protein localization as seen in seven standard subcellular compartments, including in the apoptosis and the overexpression  ...  ., for program development of image acquisition and Fujitsu Oita Software Laboratories Limited for program development of feature extraction.  ... 
doi:10.1273/cbij.4.27 fatcat:l5bakzjwsjbw5fm6fojdnc7b74

Automated interpretation of subcellular patterns from immunofluorescence microscopy

Yanhua Hu, Robert F Murphy
2004 JIM - Journal of Immunological Methods  
Supervised machine learning methods can be used to learn from the features of training images and make predictions of protein location for images not used for training.  ...  To facilitate more rapid, objective, and sensitive analysis, computer programs have been developed that can identify and compare protein subcellular locations from fluorescence microscope images.  ...  grants R01 GM068845 and R33 CA83219, and by a research grant from the Commonwealth of Pennsylvania Tobacco Settlement Fund.  ... 
doi:10.1016/j.jim.2004.04.011 pmid:15261574 fatcat:735kwl7jmng4jgfffum2ybh46u

Automated classification of protein subcellular localization in immunohistochemistry images to reveal biomarkers in colon cancer

Zhen-Zhen Xue, Yanxia Wu, Qing-Zu Gao, Liang Zhao, Ying-Ying Xu
2020 BMC Bioinformatics  
Conclusions Our results demonstrate that using image patches and combining predefined and deep features can improve the performance of protein subcellular localization, and our model can effectively detect  ...  The model uses representative image patches as inputs, and integrates feature engineering and deep learning methods. It achieves 92.69% accuracy in classification of new proteins.  ...  Availability of data and materials The datasets and code used in this study are available at https://github.com/Xue-zhen-zhen/Protein-subcellular-location Ethics approval and consent to participate Not  ... 
doi:10.1186/s12859-020-03731-y pmid:32907537 fatcat:3jmthokgavhgnk5ke5pvzwnzse

SYSTEMATIC DESCRIPTION OF SUBCELLULAR LOCATION FOR INTEGRATION WITH PROTEOMICS DATABASES AND SYSTEMS BIOLOGY MODELING

Robert F. Murphy
2007 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro  
This in turn creates a need for methods to connect these automatically determined locations to existing information in literature and databases, and to communicate the nature of the pattern for each family  ...  This permits proteins to be grouped into Subcellular Location Families that share a statistically-indistinguishable pattern.  ...  Perhaps most importantly, this work established that features drawn from a range of image analysis approaches could adequately capture the complex, highly variable location patterns displayed by proteins  ... 
doi:10.1109/isbi.2007.357036 dblp:conf/isbi/Murphy07a fatcat:2e3flhmczjb3fe2opfau3ehq6a

A reference library for assigning protein subcellular localizations by image-based machine learning

Wiebke Schormann, Santosh Hariharan, David W. Andrews
2020 Journal of Cell Biology  
Machine learning algorithms using these features permit automated assignment of the localization of other proteins and dyes in both cell types with very high accuracy.  ...  Confocal micrographs of EGFP fusion proteins localized at key cell organelles in murine and human cells were acquired for use as subcellular localization landmarks.  ...  Hariharan performed experiments, imaged the cells, and analyzed the data. W. Schormann generated all of the mutants and cell lines. S.  ... 
doi:10.1083/jcb.201904090 pmid:31968357 pmcid:PMC7055006 fatcat:cipwlnqbtjdbpmi7saltb2vtfa

Boosting accuracy of automated classification of fluorescence microscope images for location proteomics

Kai Huang, Robert F Murphy
2004 BMC Bioinformatics  
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.  ...  Ten-fold cross validation was used to evaluate each classifier with various parameters on different Subcellular Location Feature sets representing both 2D and 3D fluorescence microscope images, including  ...  Acknowledgments We thank William Dirks and Adrienne Wells for programming and preliminary work on applying wavelet features to protein location patterns, and Dr.  ... 
doi:10.1186/1471-2105-5-78 pmid:15207009 pmcid:PMC449699 fatcat:jcedg5tzcfbudojmzwcxvozmru
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