ArticlePloS one2013
An ensemble method for predicting subnuclear localizations from primary protein structures.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
- Determining human-coronavirus protein-protein interaction using machine intelligence.Medicine in novel technology and devices · 2023Article
- Ensemble Learning for Hormone Binding Protein Prediction: A Promising Approach for Early Diagnosis of Thyroid Hormone Disorders in Serum.Diagnostics (Basel, Switzerland) · 2023Article
- iT3SE-PX: Identification of Bacterial Type III Secreted Effectors Using PSSM Profiles and XGBoost Feature Selection.Computational and mathematical methods in medicine · 2021Article
- TargetCPP: accurate prediction of cell-penetrating peptides from optimized multi-scale features using gradient boost decision tree.Journal of computer-aided molecular design · 2020Article
- In Silico Rational Design and Virtual Screening of Bioactive Peptides Based on QSAR Modeling.ACS omega · 2020Article
- Identification of pre-microRNAs by characterizing their sequence order evolution information and secondary structure graphs.BMC bioinformatics · 2018Article
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- A Two-Step Feature Selection Method to Predict Cancerlectins by Multiview Features and Synthetic Minority Oversampling Technique.BioMed research international · 2018Article
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- A Novel Feature Extraction Method with Feature Selection to Identify Golgi-Resident Protein Types from Imbalanced Data.International journal of molecular sciences · 2016Article
- Analysis and Identification of Aptamer-Compound Interactions with a Maximum Relevance Minimum Redundancy and Nearest Neighbor Algorithm.BioMed research international · 2016Article
- Protein Sub-Nuclear Localization Based on Effective Fusion Representations and Dimension Reduction Algorithm LDA.International journal of molecular sciences · 2015Article
- An Ensemble Method to Distinguish Bacteriophage Virion from Non-Virion Proteins Based on Protein Sequence Characteristics.International journal of molecular sciences · 2015Article
- An Effective Antifreeze Protein Predictor with Ensemble Classifiers and Comprehensive Sequence Descriptors.International journal of molecular sciences · 2015Article
- Characterization of the nuclear matrix targeting sequence (NMTS) of the BPV1 E8/E2 protein--the shortest known NMTS.Nucleus (Austin, Tex.) · 2015Article
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- Prediction of drugs target groups based on ChEBI ontology.BioMed research international · 2013Article
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5 authors.
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Abstract
backgroundPredicting protein subnuclear localization is a challenging problem. Some previous works based on non-sequence information including Gene Ontology annotations and kernel fusion have respective limitations. The aim of this work is twofold: one is to propose a novel individual feature extraction method; another is to develop an ensemble method to improve prediction performance using comprehensive information represented in the form of high dimensional feature vector obtained by 11 feature extraction methods. METHODOLOGY/PRINCIPAL
findingsA novel two-stage multiclass support vector machine is proposed to predict protein subnuclear localizations. It only considers those feature extraction methods based on amino acid classifications and physicochemical properties. In order to speed up our system, an automatic search method for the kernel parameter is used. The prediction performance of our method is evaluated on four datasets: Lei dataset, multi-localization dataset, SNL9 dataset and a new independent dataset. The overall accuracy of prediction for 6 localizations on Lei dataset is 75.2% and that for 9 localizations on SNL9 dataset is 72.1% in the leave-one-out cross validation, 71.7% for the multi-localization dataset and 69.8% for the new independent dataset, respectively. Comparisons with those existing methods show that our method performs better for both single-localization and multi-localization proteins and achieves more balanced sensitivities and specificities on large-size and small-size subcellular localizations. The overall accuracy improvements are 4.0% and 4.7% for single-localization proteins and 6.5% for multi-localization proteins. The reliability and stability of our classification model are further confirmed by permutation analysis.
conclusionsIt can be concluded that our method is effective and valuable for predicting protein subnuclear localizations. A web server has been designed to implement the proposed method. It is freely available at http://bioinformatics.awowshop.com/snlpred_page.php.
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