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 [2], mGOASVM [3], HybridGO-Loc [4], R3P-Loc [5] and mPLR-Loc [6]. Each predictor can predict protein subcellular localization for two species. Details about PolyU-Loc can be found in our recent book [1].

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 [7] and [8] for detailed information.

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 [9] 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.

References:

  1. S. Wan and M. W. Mak, " Machine Learning for Protein Subcellular Localization Prediction", De Gruyter, ISBN 978-1-5015-0150-0, 2015, Germany.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. S. Wan, M. W. Mak, and S. Y. Kung, "Sparse Regressions for Predicting and Interpreting Subcellular Localization of Multi-Label Proteins", BMC Bioinformatics, 2016. (accepted)

  9. 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)

 

Contact @ Shibiao Wan, Man-Wai Mak,

URL: http://www.eie.polyu.edu.hk/~mwmak/

Dept. of EIE, The Hong Kong Polytechnic University

Last update: 26 Jan. 2016