Bioimage-based protein subcellular location prediction: a comprehensive review
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. Co...
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| Published in: | Frontiers of Computer Science Vol. 12; no. 1; pp. 26 - 39 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Beijing
Higher Education Press
01.02.2018
Springer Nature B.V |
| Subjects: | |
| ISSN: | 2095-2228, 2095-2236 |
| Online Access: | Get full text |
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| Summary: | 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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| Bibliography: | bioimage informatics protein subcellular location prediction multi-location protein recognition Document accepted on :2016-11-02 Document received on :2016-06-14 global and local features ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 2095-2228 2095-2236 |
| DOI: | 10.1007/s11704-016-6309-5 |