FUEL-mLoc: feature-unified prediction and explanation of multi-localization of cellular proteins in multiple organisms

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Abstract

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.

Availability and Implementation:

http://bioinfo.eie.polyu.edu.hk/FUEL-mLoc/

Contacts:

shibiao.wan@princeton.edu or enmwmak@polyu.edu.hk

Supplementary information:

Supplementary data are available at Bioinformatics online.

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