ArticleBMC bioinformatics2008
ESLpred2: improved method for predicting subcellular localization of eukaryotic proteins.
Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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24 citing papers in PubMed.
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- PredPSD: A Gradient Tree Boosting Approach for Single-Stranded and Double-Stranded DNA Binding Protein Prediction.Molecules (Basel, Switzerland) · 2019Article
- Harnessing the evolutionary information on oxygen binding proteins through Support Vector Machines based modules.BMC research notes · 2018Article
- Analysis and prediction of single-stranded and double-stranded DNA binding proteins based on protein sequences.BMC bioinformatics · 2017Article
- Fast H-DROP: A thirty times accelerated version of H-DROP for interactive SVM-based prediction of helical domain linkers.Journal of computer-aided molecular design · 2017Article
- The effector candidate repertoire of the arbuscular mycorrhizal fungus Rhizophagus clarus.BMC genomics · 2016Article
- A ComparativeEvolutionary bioinformatics online · 2016Article
- Protein (multi-)location prediction: utilizing interdependencies via a generative model.Bioinformatics (Oxford, England) · 2015Article
- Identification of a Novel Small Cysteine-Rich Protein in the Fraction from the Biocontrol Fusarium oxysporum Strain CS-20 that Mitigates Fusarium Wilt Symptoms and Triggers Defense Responses in Tomato.Frontiers in plant science · 2015Article
- Proteomic analysis of the Plasmodium male gamete reveals the key role for glycolysis in flagellar motility.Malaria journal · 2014Article
- Protein (multi-)location prediction: using location inter-dependencies in a probabilistic framework.Algorithms for molecular biology : AMB · 2014Article
- Molecular characterization and identification of Th1 epitopes of a Schistosoma japonicum protein similar to prosaposin.Parasitology research · 2014Article
- CELLO2GO: a web server for protein subCELlular LOcalization prediction with functional gene ontology annotation.PloS one · 2014Article
- EuLoc: a web-server for accurately predict protein subcellular localization in eukaryotes by incorporating various features of sequence segments into the general form of Chou's PseAAC.Journal of computer-aided molecular design · 2013Article
- Proteomic analysis of Plasmodium in the mosquito: progress and pitfalls.Parasitology · 2012Article
- Mining Functional Elements in Messenger RNAs: Overview, Challenges, and Perspectives.Frontiers in plant science · 2011Article
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- YLoc--an interpretable web server for predicting subcellular localization.Nucleic acids research · 2010Article
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Abstract
backgroundThe expansion of raw protein sequence databases in the post genomic era and availability of fresh annotated sequences for major localizations particularly motivated us to introduce a new improved version of our previously forged eukaryotic subcellular localizations prediction method namely "ESLpred". Since, subcellular localization of a protein offers essential clues about its functioning, hence, availability of localization predictor would definitely aid and expedite the protein deciphering studies. However, robustness of a predictor is highly dependent on the superiority of dataset and extracted protein attributes; hence, it becomes imperative to improve the performance of presently available method using latest dataset and crucial input features.
resultsHere, we describe augmentation in the prediction performance obtained for our most popular ESLpred method using new crucial features as an input to Support Vector Machine (SVM). In addition, recently available, highly non-redundant dataset encompassing three kingdoms specific protein sequence sets; 1198 fungi sequences, 2597 from animal and 491 plant sequences were also included in the present study. First, using the evolutionary information in the form of profile composition along with whole and N-terminal sequence composition as an input feature vector of 440 dimensions, overall accuracies of 72.7, 75.8 and 74.5% were achieved respectively after five-fold cross-validation. Further, enhancement in performance was observed when similarity search based results were coupled with whole and N-terminal sequence composition along with profile composition by yielding overall accuracies of 75.9, 80.8, 76.6% respectively; best accuracies reported till date on the same datasets.
conclusionThese results provide confidence about the reliability and accurate prediction of SVM modules generated in the present study using sequence and profile compositions along with similarity search based results. The presently developed modules are implemented as web server "ESLpred2" available at http://www.imtech.res.in/raghava/eslpred2/.
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