Journal
BIOINFORMATICS
Volume 33, Issue 5, Pages 749-750Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btw717
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Funding
- Hong Kong Research Grant Council [PolyU152068/15E, PolyU152518/16E]
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Although many web-servers for predicting protein subcellular localization have been developed, they often have the following drawbacks: (i) lack of interpretability or interpreting results with heterogenous information which may confuse users; (ii) ignoring multi-location proteins and (iii) only focusing on specific organism. To tackle these problems, we present an interpretable and efficient web-server, namely FUEL-mLoc, using Feature-Unified prediction and Explanation of multi-Localization of cellular proteins in multiple organisms. Compared to conventional localization predictors, FUEL-mLoc has the following advantages: (i) using unified features (i.e. essential GO terms) to interpret why a prediction is made; (ii) being capable of predicting both single-and multi-location proteins and (iii) being able to handle proteins of multiple organisms, including Eukaryota, Homo sapiens, Viridiplantae, Gram-positive Bacteria, Gram-negative Bacteria and Virus. Experimental results demonstrate that FUEL-mLoc outperforms state-of-the-art subcellular-localization predictors.
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