ArticlePloS one2012
A multi-label predictor for identifying the subcellular locations of singleplex and multiplex eukaryotic proteins.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Classifying Multifunctional Enzymes by Incorporating Three Different Models into Chou's General Pseudo Amino Acid Composition.The Journal of membrane biology · 2016Article
- Human Protein Subcellular Localization with Integrated Source and Multi-label Ensemble Classifier.Scientific reports · 2016Article
- Sparse regressions for predicting and interpreting subcellular localization of multi-label proteins.BMC bioinformatics · 2016Article
- Predicting Subcellular Localization of Apoptosis Proteins Combining GO Features of Homologous Proteins and Distance Weighted KNN Classifier.BioMed research international · 2016Article
- A new multi-label classifier in identifying the functional types of human membrane proteins.The Journal of membrane biology · 2015Article
- Multi-location gram-positive and gram-negative bacterial protein subcellular localization using gene ontology and multi-label classifier ensemble.BMC bioinformatics · 2015Article
- Syndrome Differentiation Analysis on Mars500 Data of Traditional Chinese Medicine.TheScientificWorldJournal · 2015Article
- A multi-label classifier for prediction membrane protein functional types in animal.The Journal of membrane biology · 2014Article
- Article
- HybridGO-Loc: mining hybrid features on gene ontology for predicting subcellular localization of multi-location proteins.PloS one · 2014Article
- Prediction of gene phenotypes based on GO and KEGG pathway enrichment scores.BioMed research international · 2013Article
- SubMito-PSPCP: predicting protein submitochondrial locations by hybridizing positional specific physicochemical properties with pseudoamino acid compositions.BioMed research international · 2013Article
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2 authors.
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Abstract
Subcellular locations of proteins are important functional attributes. An effective and efficient subcellular localization predictor is necessary for rapidly and reliably annotating subcellular locations of proteins. Most of existing subcellular localization methods are only used to deal with single-location proteins. Actually, proteins may simultaneously exist at, or move between, two or more different subcellular locations. To better reflect characteristics of multiplex proteins, it is highly desired to develop new methods for dealing with them. In this paper, a new predictor, called Euk-ECC-mPLoc, by introducing a powerful multi-label learning approach which exploits correlations between subcellular locations and hybridizing gene ontology with dipeptide composition information, has been developed that can be used to deal with systems containing both singleplex and multiplex eukaryotic proteins. It can be utilized to identify eukaryotic proteins among the following 22 locations: (1) acrosome, (2) cell membrane, (3) cell wall, (4) centrosome, (5) chloroplast, (6) cyanelle, (7) cytoplasm, (8) cytoskeleton, (9) endoplasmic reticulum, (10) endosome, (11) extracellular, (12) Golgi apparatus, (13) hydrogenosome, (14) lysosome, (15) melanosome, (16) microsome, (17) mitochondrion, (18) nucleus, (19) peroxisome, (20) spindle pole body, (21) synapse, and (22) vacuole. Experimental results on a stringent benchmark dataset of eukaryotic proteins by jackknife cross validation test show that the average success rate and overall success rate obtained by Euk-ECC-mPLoc were 69.70% and 81.54%, respectively, indicating that our approach is quite promising. Particularly, the success rates achieved by Euk-ECC-mPLoc for small subsets were remarkably improved, indicating that it holds a high potential for simulating the development of the area. As a user-friendly web-server, Euk-ECC-mPLoc is freely accessible to the public at the website http://levis.tongji.edu.cn:8080/bioinfo/Euk-ECC-mPLoc/. We believe that Euk-ECC-mPLoc may become a useful high-throughput tool, or at least play a complementary role to the existing predictors in identifying subcellular locations of eukaryotic proteins.
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