Literature DB >> 19764776

SherLoc2: a high-accuracy hybrid method for predicting subcellular localization of proteins.

Sebastian Briesemeister1, Torsten Blum, Scott Brady, Yin Lam, Oliver Kohlbacher, Hagit Shatkay.   

Abstract

SherLoc2 is a comprehensive high-accuracy subcellular localization prediction system. It is applicable to animal, fungal, and plant proteins and covers all main eukaryotic subcellular locations. SherLoc2 integrates several sequence-based features as well as text-based features. In addition, we incorporate phylogenetic profiles and Gene Ontology (GO) terms derived from the protein sequence to considerably improve the prediction performance. SherLoc2 achieves an overall classification accuracy of up to 93% in 5-fold cross-validation. A novel feature, DiaLoc, allows users to manually provide their current background knowledge by describing a protein in a short abstract which is then used to improve the prediction. SherLoc2 is available both as a free Web service and as a stand-alone version at http://www-bs.informatik.uni-tuebingen.de/Services/SherLoc2.

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Year:  2009        PMID: 19764776     DOI: 10.1021/pr900665y

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  41 in total

1.  The SubCons webserver: A user friendly web interface for state-of-the-art subcellular localization prediction.

Authors:  M Salvatore; N Shu; A Elofsson
Journal:  Protein Sci       Date:  2017-10-24       Impact factor: 6.725

2.  Going from where to why--interpretable prediction of protein subcellular localization.

Authors:  Sebastian Briesemeister; Jörg Rahnenführer; Oliver Kohlbacher
Journal:  Bioinformatics       Date:  2010-03-17       Impact factor: 6.937

3.  YLoc--an interpretable web server for predicting subcellular localization.

Authors:  Sebastian Briesemeister; Jörg Rahnenführer; Oliver Kohlbacher
Journal:  Nucleic Acids Res       Date:  2010-05-27       Impact factor: 16.971

Review 4.  Understanding molecular mechanisms of disease through spatial proteomics.

Authors:  Sandra Pankow; Salvador Martínez-Bartolomé; Casimir Bamberger; John R Yates
Journal:  Curr Opin Chem Biol       Date:  2018-10-09       Impact factor: 8.822

5.  An introduction to deep learning on biological sequence data: examples and solutions.

Authors:  Vanessa Isabell Jurtz; Alexander Rosenberg Johansen; Morten Nielsen; Jose Juan Almagro Armenteros; Henrik Nielsen; Casper Kaae Sønderby; Ole Winther; Søren Kaae Sønderby
Journal:  Bioinformatics       Date:  2017-11-15       Impact factor: 6.937

6.  Protein Subcellular Localization Prediction.

Authors:  Elettra Barberis; Emilio Marengo; Marcello Manfredi
Journal:  Methods Mol Biol       Date:  2021

7.  ngLOC: software and web server for predicting protein subcellular localization in prokaryotes and eukaryotes.

Authors:  Brian R King; Suleyman Vural; Sanjit Pandey; Alex Barteau; Chittibabu Guda
Journal:  BMC Res Notes       Date:  2012-07-10

8.  Comparative analysis of an experimental subcellular protein localization assay and in silico prediction methods.

Authors:  Yuhui Hu; Hans Lehrach; Michal Janitz
Journal:  J Mol Histol       Date:  2009-12-22       Impact factor: 2.611

9.  LocTree2 predicts localization for all domains of life.

Authors:  Tatyana Goldberg; Tobias Hamp; Burkhard Rost
Journal:  Bioinformatics       Date:  2012-09-15       Impact factor: 6.937

10.  Mining Proteins with Non-Experimental Annotations Based on an Active Sample Selection Strategy for Predicting Protein Subcellular Localization.

Authors:  Junzhe Cao; Wenqi Liu; Jianjun He; Hong Gu
Journal:  PLoS One       Date:  2013-06-26       Impact factor: 3.240

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