Welcome to PolyU-Loc server!
PolyU-Loc is a package of web-servers for predicting
subcellular localization of single-location and multi-location
proteins in different species, such as Homo sapiens,
Viridiplantae, Eukaryota and Virus. It originally
consists of five web-servers, i.e., including GOASVM ,
mGOASVM , HybridGO-Loc , R3P-Loc 
and mPLR-Loc . Each predictor can predict protein
subcellular localization for two species. Details about
PolyU-Loc can be found in our recent book .
Recently, two new web-servers, namely mLASSO-Hum and SpaPredictor,
are incorporated into PolyU-Loc. Both mLASSO-Hum and
SpaPredictor are designed to specifically predict subcellular
localization of single-location and multi-location human
proteins. Different from the previous five predictors, both of
them are interpretable predictors. See the paper  and  for
Besides, PolyU-Loc includes a web-server, namely EnTrans-Chlo
, which is specifically designed to predict subchloroplast
localization of both single- and multi-location chloroplast
proteins. In particular, EnTrans-Chlo is a transductive-learning
based predictor. See the paper  for detailed information.
Information about PolyU-Loc can be found as follows:
- Servers can be found here.
- General instructions can be found here.
- All the supplementary materials can be found here.
- Related publications can be found here.
- S. Wan and M. W. Mak, " Machine Learning for Protein
Subcellular Localization Prediction", De Gruyter,
ISBN 978-1-5015-0150-0, 2015, Germany.
- S. Wan, M. W. Mak, and S. Y. Kung, "GOASVM: A
Subcellular Location Predictor by Incorporating Term-Frequency
Gene Ontology into the General Form of Chou’s Pseudo-Amino Acid
Composition", Journal of Theoretical Biology,
2013, vol. 323, pp. 40–48.
- S. Wan, M. W. Mak, and S. Y. Kung, "mGOASVM:
Multi-label Protein Subcellular Localization Based on Gene
Ontology and Support Vector Machines", BMC
Bioinformatics, 2012, 13:290.
- S. Wan, M. W. Mak, and S. Y. Kung, "HybridGO-Loc:
Mining Hybrid Features on Gene Ontology for Predicting
Subcellular Localization of Multi-Location Proteins", PLoS
ONE, 2014, 9(3): e89545.
- S. Wan, M. W. Mak, and S. Y. Kung, "R3P-Loc: A Compact
Multi-label Predictor Using Ridge Regression and Random
Projection for Protein Subcellular Localization", Journal
of Theoretical Biology, 2014, vol.360, pp. 34-45.
- S. Wan, M. W. Mak, and S. Y. Kung, " mPLR-Loc: An
Adaptive-decision Multi-label Classifier Based on Penalized
Logistic Regression for Protein Subcellular Localization
Prediction", Analytical Biochemistry, 2015, vol.
473, pp. 14-27.
- S. Wan, M. W. Mak, and S. Y. Kung, "mLASSO-Hum: A
LASSO-Based Interpretable Human-Protein Subcellular
Localization Predictor", Journal of Theoretical
Biology, 2015, vol. 382, pp. 223-234.
- S. Wan, M. W. Mak, and S. Y. Kung, "Sparse Regressions
for Predicting and Interpreting Subcellular Localization of
Multi-Label Proteins", BMC Bioinformatics, 2016.
- S. Wan, M. W. Mak, and S. Y. Kung, "Transductive
Learning for Multi-Label Protein Subchloroplast Localization
Prediction", IEEE/ACM Transactions on
Computational Biology and Bioinformatics, 2016. (accepted)