ArticleBMC bioinformatics2005
pSLIP: SVM based protein subcellular localization prediction using multiple physicochemical properties.
Article in BMC bioinformatics, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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31 citing papers in PubMed.
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- Machine learning and comparative genomics approaches for the discovery of xylose transporters in yeast.Biotechnology for biofuels and bioproducts · 2022Article
- PFP-GO: Integrating protein sequence, domain and protein-protein interaction information for protein function prediction using ranked GO terms.Frontiers in genetics · 2022Article
- Estimating the Influence of Physicochemical and Biochemical Property Indexes on Selection for Amino Acids Usage in Eukaryotic Cells.Journal of molecular evolution · 2021Article
- Computational methods for protein localization prediction.Computational and structural biotechnology journal · 2021Review
- 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
- Classification of adaptor proteins using recurrent neural networks and PSSM profiles.BMC genomics · 2019Article
- Low-dimensional representation of genomic sequences.Journal of mathematical biology · 2019Article
- UniProt-DAAC: domain architecture alignment and classification, a new method for automatic functional annotation in UniProtKB.Bioinformatics (Oxford, England) · 2016Article
- A survey of computational intelligence techniques in protein function prediction.International journal of proteomics · 2014Review
- Relationship between Metabolic Fluxes and Sequence-Derived Properties of Enzymes.International scholarly research notices · 2014Article
- PlantLoc: an accurate web server for predicting plant protein subcellular localization by substantiality motif.Nucleic acids research · 2013Article
- An ensemble method for predicting subnuclear localizations from primary protein structures.PloS one · 2013Article
- TESTLoc: protein subcellular localization prediction from EST data.BMC bioinformatics · 2010Article
- Combining machine learning and homology-based approaches to accurately predict subcellular localization in Arabidopsis.Plant physiology · 2010Article
- MiRTif: a support vector machine-based microRNA target interaction filter.BMC bioinformatics · 2008Article
- ESLpred2: improved method for predicting subcellular localization of eukaryotic proteins.BMC bioinformatics · 2008Article
- IRES-mediated translation of the carboxy-terminal domain of the horizontal cell specific connexin Cx55.5 in vivo and in vitro.BMC molecular biology · 2008Article
- DBMLoc: a Database of proteins with multiple subcellular localizations.BMC bioinformatics · 2008Article
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4 authors.
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Abstract
backgroundProtein subcellular localization is an important determinant of protein function and hence, reliable methods for prediction of localization are needed. A number of prediction algorithms have been developed based on amino acid compositions or on the N-terminal characteristics (signal peptides) of proteins. However, such approaches lead to a loss of contextual information. Moreover, where information about the physicochemical properties of amino acids has been used, the methods employed to exploit that information are less than optimal and could use the information more effectively.
resultsIn this paper, we propose a new algorithm called pSLIP which uses Support Vector Machines (SVMs) in conjunction with multiple physicochemical properties of amino acids to predict protein subcellular localization in eukaryotes across six different locations, namely, chloroplast, cytoplasmic, extracellular, mitochondrial, nuclear and plasma membrane. The algorithm was applied to the dataset provided by Park and Kanehisa and we obtained prediction accuracies for the different classes ranging from 87.7%-97.0% with an overall accuracy of 93.1%.
conclusionThis study presents a physicochemical property based protein localization prediction algorithm. Unlike other algorithms, contextual information is preserved by dividing the protein sequences into clusters. The prediction accuracy shows an improvement over other algorithms based on various types of amino acid composition (single, pair and gapped pair). We have also implemented a web server to predict protein localization across the six classes (available at http://pslip.bii.a-star.edu.sg/).
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