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dc.contributor.authorTsai, Yuh-Showen_US
dc.contributor.authorChung, I-Fangen_US
dc.contributor.authorSimpson, Jeremy C.en_US
dc.contributor.authorLee, Mei-Ien_US
dc.contributor.authorHsiung, Chia-Chengen_US
dc.contributor.authorChiu, Tai-Yuen_US
dc.contributor.authorKao, Lung-Senen_US
dc.contributor.authorChiu, Te-Chengen_US
dc.contributor.authorLin, Chin-Tengen_US
dc.contributor.authorLin, Wen-Chiehen_US
dc.contributor.authorLiang, Sheng-Fuen_US
dc.contributor.authorLin, Chung-Chihen_US
dc.date.accessioned2014-12-08T15:12:20Z-
dc.date.available2014-12-08T15:12:20Z-
dc.date.issued2008-04-01en_US
dc.identifier.issn1059-910Xen_US
dc.identifier.urihttp://dx.doi.org/10.1002/jemt.20555en_US
dc.identifier.urihttp://hdl.handle.net/11536/9480-
dc.description.abstractSystemic analysis of subcellular protein localization (location proteomics) provides clues for understanding gene functions and physiological condition of the cells. However, recognition of cell images of subcellular structures highly depends on experience and becomes the rate-limiting step when classifying subcellular protein localization. Several research groups have extracted specific numerical features for the recognition of subcellular protein localization, but these recognition systems are restricted to images of single particular cell line acquired by one specific imaging system and not applied to recognize a range of cell image sources. In this study, we establish a single system for automated subcellular structure recognition to identify cell images from various sources. Two different sources of cell images, 317 Vero (http://gfp-cdna.embl.de) and 875 CHO cell images of subcellular structures, were used to train and test the system. When the system was trained by a single source of images, the recognition rate is high and specific to the trained source. The system trained by the CHO cell images gave high average recognition accuracy for CHO cells of 96%, but this was reduced to 46% with Vero images. When we trained the system using a mixture of CHO and Vero cell images, an average accuracy of recognition reached 86.6% for both CHO and Vero cell images. The system can reject images with low confidence and identify the cell images correctly recognized to avoid manual reconfirmation. In summary, we have established a single system that can recognize subcellular protein localizations from two different sources for location-proteomic studies.en_US
dc.language.isoen_USen_US
dc.subjectsubcellular featuresen_US
dc.subjectautomated recognitionen_US
dc.subjectCHO cellsen_US
dc.subjectVero cellsen_US
dc.subjectGFPen_US
dc.subjectrejection rateen_US
dc.titleAutomated recognition system to classify subcellular protein localizations in images of different cell lines acquired by different imaging systemsen_US
dc.typeArticleen_US
dc.identifier.doi10.1002/jemt.20555en_US
dc.identifier.journalMICROSCOPY RESEARCH AND TECHNIQUEen_US
dc.citation.volume71en_US
dc.citation.issue4en_US
dc.citation.spage305en_US
dc.citation.epage314en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000254964100008-
dc.citation.woscount2-
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