Tag: multi-localization

FUEL-mLoc – Feature-Unified Prediction and Explanation of multi-Localization of Cellular Proteins in multiple Organisms

FUEL-mLoc

:: DESCRIPTION

FUEL-mLoc is an interpretable multi-label predictor which uses unified features to yield sparse and interpretable solutions for large-scale prediction of both single-label and multi-label proteins of different species, including eukaryota, human, plant, Gram-positive bacteria. Gram-negative bacteria and virus. Given a query protein sequence in a particular species, a set of GO terms are retrieved from a newly created compact databases, namely ProSeq-GO.

::DEVELOPER

Dr. Man-Wai Mak

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

Wan S, Mak MW, Kung SY.
FUEL-mLoc: feature-unified prediction and explanation of multi-localization of cellular proteins in multiple organisms.
Bioinformatics. 2017 Mar 1;33(5):749-750. doi: 10.1093/bioinformatics/btw717. PMID: 28011780.

Gram-LocEN – Interpretable prediction of subcellular multi-localization of Gram-positive and Gram-negative bacterial proteins

Gram-LocEN

:: DESCRIPTION

Gram-LocEN is an interpretable multi-label predictor which uses unified features to yield sparse and interpretable solutions for large-scale prediction of both single-label and multi-label proteins of different species, including Gram-positive bacteria and Gram-negative bacteria. Given a query protein sequence in a particular species, a set of GO terms are retrieved from a newly created compact databases, namely ProSeq-GO.

::DEVELOPER

Dr. Man-Wai Mak

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Web browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

S. Wan, M. W. Mak, and S. Y. Kung,
“Gram-LocEN: Interpretable prediction of subcellular multi-localization of Gram-positive and Gram-negative bacterial proteins”
2016, submitted.