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Automated recognition system to classify subcellular protein localizations in images of different cell lines acquired by different imaging systems
Author(s) -
Tsai YuhShow,
Chung IFang,
Simpson Jeremy C.,
Lee MeiI,
Hsiung ChiaCheng,
Chiu TaiYu,
Kao LungSen,
Chiu TeCheng,
Lin ChinTeng,
Lin WenChieh,
Liang ShengFu,
Lin ChungChih
Publication year - 2008
Publication title -
microscopy research and technique
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.536
H-Index - 118
eISSN - 1097-0029
pISSN - 1059-910X
DOI - 10.1002/jemt.20555
Subject(s) - subcellular localization , artificial intelligence , protein subcellular localization prediction , pattern recognition (psychology) , vero cell , chinese hamster ovary cell , computer vision , computer science , biology , cell culture , gene , microbiology and biotechnology , cytoplasm , biochemistry , genetics
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. studies.