Title: Automated recognition system to classify subcellular protein localizations in images of different cell lines acquired by different imaging systems
Authors: Tsai, Yuh-Show
Chung, I-Fang
Simpson, Jeremy C.
Lee, Mei-I
Hsiung, Chia-Cheng
Chiu, Tai-Yu
Kao, Lung-Sen
Chiu, Te-Cheng
Lin, Chin-Teng
Lin, Wen-Chieh
Liang, Sheng-Fu
Lin, Chung-Chih
資訊工程學系
Department of Computer Science
Keywords: subcellular features;automated recognition;CHO cells;Vero cells;GFP;rejection rate
Issue Date: 1-Apr-2008
Abstract: Systemic 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.
URI: http://dx.doi.org/10.1002/jemt.20555
http://hdl.handle.net/11536/9480
ISSN: 1059-910X
DOI: 10.1002/jemt.20555
Journal: MICROSCOPY RESEARCH AND TECHNIQUE
Volume: 71
Issue: 4
Begin Page: 305
End Page: 314
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