ArticleMolecular diversity2010
Prediction of subcellular location of mycobacterial protein using feature selection techniques.
Article in Molecular diversity, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 43 citations in OpenAlex.
- Recent development of machine learning-based methods for the prediction of defensin family and subfamily.EXCLI journal · 2022Review
- Prediction of subcellular location of apoptosis proteins by incorporating PsePSSM and DCCA coefficient based on LFDA dimensionality reduction.BMC genomics · 2018Article
- iRSpot-Pse6NC: Identifying recombination spots inInternational journal of biological sciences · 2018Article
- High-Performance Prediction of Human Estrogen Receptor Agonists Based on Chemical Structures.Molecules (Basel, Switzerland) · 2017Article
- A novel feature ranking method for prediction of cancer stages using proteomics data.PloS one · 2017Article
- Predicting the Types of Ion Channel-Targeted Conotoxins Based on AVC-SVM Model.BioMed research international · 2017Article
- Prediction of aptamer-protein interacting pairs using an ensemble classifier in combination with various protein sequence attributes.BMC bioinformatics · 2016Article
- Metabolomic Profiling of Plasma from Patients with Tuberculosis by Use of Untargeted Mass Spectrometry Reveals Novel Biomarkers for Diagnosis.Journal of clinical microbiology · 2015Article
- Survey of Natural Language Processing Techniques in Bioinformatics.Computational and mathematical methods in medicine · 2015Review
- Identification of specific metabolites in culture supernatant of Mycobacterium tuberculosis using metabolomics: exploration of potential biomarkers.Emerging microbes & infections · 2015Article
- Identifying the subfamilies of voltage-gated potassium channels using feature selection technique.International journal of molecular sciences · 2014Article
- Protein binding site prediction by combining hidden Markov support vector machine and profile-based propensities.TheScientificWorldJournal · 2014Article
- iCTX-type: a sequence-based predictor for identifying the types of conotoxins in targeting ion channels.BioMed research international · 2014Article
- Comparative genomics analysis of Mycobacterium ulcerans for the identification of putative essential genes and therapeutic candidates.PloS one · 2012Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mycobacterium tuberculosis is the primary pathogen causing tuberculosis, which is one of the most prevalent infectious diseases. The subcellular location of mycobacterial proteins can provide essential clues for proteins function research and drug discovery. Therefore, it is highly desirable to develop a computational method for fast and reliable prediction of subcellular location of mycobacterial proteins. In this study, we developed a support vector machine (SVM) based method to predict subcellular location of mycobacterial proteins. A total of 444 non-redundant mycobacterial proteins were used to train and test proposed model by using jackknife cross validation. By selecting traditional pseudo amino acid composition (PseAAC) as parameters, the overall accuracy of 83.3% was achieved. Moreover, a feature selection technique was developed to find out an optimal amount of PseAAC for improving predictive performance. The optimal amount of PseAAC improved overall accuracy from 83.3 to 87.2%. In addition, the reduced amino acids in N-terminus and non N-terminus of proteins were combined in models for further improving predictive successful rate. As a result, the maximum overall accuracy of 91.2% was achieved with average accuracy of 79.7%. The proposed model provides highly useful information for further experimental research. The prediction model can be accessed free of charge at http://cobi.uestc.edu.cn/cobi/people/hlin/webserver.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.