ArticleBMC bioinformatics2015
Gram-positive and Gram-negative subcellular localization using rotation forest and physicochemical-based features.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- StackDPP: a stacking ensemble based DNA-binding protein prediction model.BMC bioinformatics · 2024Article
- Use of Chou's 5-steps rule to predict the subcellular localization of gram-negative and gram-positive bacterial proteins by multi-label learning based on gene ontology annotation and profile alignment.Journal of integrative bioinformatics · 2020Article
- Subcellular location prediction of apoptosis proteins using two novel feature extraction methods based on evolutionary information and LDA.BMC bioinformatics · 2020Article
- Protein sequence information extraction and subcellular localization prediction with gapped k-Mer method.BMC bioinformatics · 2019Article
- SumSec: Accurate Prediction of Sumoylation Sites Using Predicted Secondary Structure.Molecules (Basel, Switzerland) · 2018Article
- Success: evolutionary and structural properties of amino acids prove effective for succinylation site prediction.BMC genomics · 2018Article
- Article
- iDNAProt-ES: Identification of DNA-binding Proteins Using Evolutionary and Structural Features.Scientific reports · 2017Article
- HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features.BioMed research international · 2017Article
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Authors and funding
7 authors.
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Abstract
backgroundThe functioning of a protein relies on its location in the cell. Therefore, predicting protein subcellular localization is an important step towards protein function prediction. Recent studies have shown that relying on Gene Ontology (GO) for feature extraction can improve the prediction performance. However, for newly sequenced proteins, the GO is not available. Therefore, for these cases, the prediction performance of GO based methods degrade significantly.
resultsIn this study, we develop a method to effectively employ physicochemical and evolutionary-based information in the protein sequence. To do this, we propose segmentation based feature extraction method to explore potential discriminatory information based on physicochemical properties of the amino acids to tackle Gram-positive and Gram-negative subcellular localization. We explore our proposed feature extraction techniques using 10 attributes that have been experimentally selected among a wide range of physicochemical attributes. Finally by applying the Rotation Forest classification technique to our extracted features, we enhance Gram-positive and Gram-negative subcellular localization accuracies up to 3.4% better than previous studies which used GO for feature extraction.
conclusionBy proposing segmentation based feature extraction method to explore potential discriminatory information based on physicochemical properties of the amino acids as well as using Rotation Forest classification technique, we are able to enhance the Gram-positive and Gram-negative subcellular localization prediction accuracies, significantly.
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