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Bioimage-based protein subcellular location prediction: a comprehensive review |
Ying-Ying XU1,2, Li-Xiu YAO1,2, Hong-Bin SHEN1,2( ) |
1. Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China
2. Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China |
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Abstract Subcellular localization of proteins can provide key hints to infer their functions and structures in cells. With the breakthrough of recent molecule imaging techniques, the usage of 2D bioimages has become increasingly popular in automatically analyzing the protein subcellular location patterns. Compared with the widely used protein 1D amino acid sequence data, the images of protein distribution are more intuitive and interpretable, making the images a better choice at many applications for revealing the dynamic characteristics of proteins, such as detecting protein translocation and quantification of proteins. In this paper, we systematically reviewed the recent progresses in the field of automated image-based protein subcellular location prediction, and classified them into four categories including growing of bioimage databases, description of subcellular location distribution patterns, classification methods, and applications of the prediction systems. Besides, we also discussed some potential directions in this field.
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| Keywords
bioimage informatics
protein subcellular location prediction
global and local features
multi-location protein recognition
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Corresponding Author(s):
Hong-Bin SHEN
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Just Accepted Date: 16 November 2016
Online First Date: 07 June 2017
Issue Date: 12 January 2018
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