:: DESCRIPTION
SLIF finds fluorescence microscope images in on-line journal articles, and indexes them according to cell line, proteins visualized, and resolution.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Bioinformatics. 2008 Feb 15;24(4):569-76.
Improved recognition of figures containing fluorescence microscope images in online journal articles using graphical models.
Qian Y, Murphy RF.
:: DESCRIPTION
SLML tools implements the generative models of subcellular location
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
T. Zhao and R.F. Murphy. (2007)
Automated learning of generative models for subcellular location: Building blocks for systems biology.
Cytometry 71A:978-990.
:: DESCRIPTION
SLPFA is a predictor for subcellular location prediction of proteins by feature vectors based on amino acid composition (frequency) and sequence alignment. 90.96% of overall accuracy was obtained through fivefold cross validation tests with TargetP plant data sets.
SLPFA is an improved subcellular location predictor of SLP-Local
::DEVELOPER
Akutsu Laboratory (Laboratory of Mathematical Bioinformatics)
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
NO
:: MORE INFORMATION
Citation:
BMC Bioinformatics. 2007 Nov 30;8:466.
Subcellular location prediction of proteins using support vector machines with alignment of block sequences utilizing amino acid composition.
Tamura T, Akutsu T.
:: DESCRIPTION
MDLoc is a Dependency-Based Protein Subcellular Location Predictor
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
NO
:: MORE INFORMATION
Citation
Protein (multi-)location prediction: utilizing interdependencies via a generative model.
Simha R, Briesemeister S, Kohlbacher O, Shatkay H.
Bioinformatics. 2015 Jun 15;31(12):i365-i374. doi: 10.1093/bioinformatics/btv264.
:: DESCRIPTION
S-PSorter (or SC-PSorter ) is a novel cell structure-driven classifier construction approach for predicting image-based protein subcellular location by employing the prior biological structural information.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Human Cell Structure-driven Model Construction for Predicting Protein Subcellular Location from Biological Images.
Shao W, Liu M, Zhang D.
Bioinformatics. 2015 Sep 11. pii: btv521.
:: DESCRIPTION
NYCE predicts subcellular location (either Nuclear, Nucleo-cytoplasmic, Cytoplasmic or Extracellular) of eukaryotic proteins using the predicted exposure value of their amino acids.
::DEVELOPER
Computational Biology and Data Mining (CBDM) Group
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
NO
:: MORE INFORMATION
Citation
BMC Bioinformatics. 2013 Nov 28;14:342. doi: 10.1186/1471-2105-14-342.
A novel approach for protein subcellular location prediction using amino acid exposure.
Mer AS1, Andrade-Navarro MA.
:: DESCRIPTION
iLoc-LncRNA is a sequence-based predictor to predict the subcellular locations of LncRNAs(Long non-coding RNAs).
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
iLoc-lncRNA: predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC.
Su ZD, Huang Y, Zhang ZY, Zhao YW, Wang D, Chen W, Chou KC, Lin H.
Bioinformatics. 2018 Dec 15;34(24):4196-4204. doi: 10.1093/bioinformatics/bty508.