title: Automated recognition system to classify subcellular protein localizations in images of different cell lines acquired by different imaging systems.
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. studies.
authors: Department of Biomedical Engineering, Chung Yuan Christian University, Jhongli, Taiwan, Republic of China.
authors: Yuh-Show
authors: YS
authors: Tsai
authors: I-Fang
authors: IF
authors: Chung
authors: Jeremy C
authors: JC
authors: Simpson
authors: Mei-I
authors: MI
authors: Lee
authors: Chia-Cheng
authors: CC
authors: Hsiung
authors: Tai-Yu
authors: TY
authors: Chiu
authors: Lung-Sen
authors: LS
authors: Kao
authors: Te-Cheng
authors: TC
authors: Chiu
authors: Chin-Teng
authors: CT
authors: Lin
authors: Wen-Chieh
authors: WC
authors: Lin
authors: Sheng-Fu
authors: SF
authors: Liang
authors: Chung-Chih
authors: CC
authors: Lin
mesh_terms: D000465:Algorithms; D000818:Animals; D016466:CHO Cells; D002522:Chlorocebus aethiops; D006224:Cricetinae; D003412:Cricetulus; D049452:Green Fluorescent Proteins / Q000378:metabolism; D007090:Image Interpretation, Computer-Assisted; D008856:Microscopy, Fluorescence / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011506:Proteins / Q000378:metabolism*; D013347:Subcellular Fractions / Q000145:classification* / Q000378:metabolism / Q000648:ultrastructure*; D014709:Vero Cells
chemical_list: D011506:Proteins; D049452:Green Fluorescent Proteins