Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11851/6068
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Öztürk, Orkun | - |
dc.contributor.author | Aksaç, Alper | - |
dc.contributor.author | Elsheikh, Abdallah | - |
dc.contributor.author | Özyer, Tansel | - |
dc.contributor.author | Alhajj, Reda | - |
dc.date.accessioned | 2021-09-11T15:34:52Z | - |
dc.date.available | 2021-09-11T15:34:52Z | - |
dc.date.issued | 2013 | en_US |
dc.identifier.issn | 1932-6203 | - |
dc.identifier.uri | https://doi.org/10.1371/journal.pone.0063145 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11851/6068 | - |
dc.description.abstract | Background: Predicting type-1 Human Immunodeficiency Virus (HIV-1) protease cleavage site in protein molecules and determining its specificity is an important task which has attracted considerable attention in the research community. Achievements in this area are expected to result in effective drug design (especially for HIV-1 protease inhibitors) against this life-threatening virus. However, some drawbacks (like the shortage of the available training data and the high dimensionality of the feature space) turn this task into a difficult classification problem. Thus, various machine learning techniques, and specifically several classification methods have been proposed in order to increase the accuracy of the classification model. In addition, for several classification problems, which are characterized by having few samples and many features, selecting the most relevant features is a major factor for increasing classification accuracy. Results: We propose for HIV-1 data a consistency-based feature selection approach in conjunction with recursive feature elimination of support vector machines (SVMs). We used various classifiers for evaluating the results obtained from the feature selection process. We further demonstrated the effectiveness of our proposed method by comparing it with a state-of-the-art feature selection method applied on HIV-1 data, and we evaluated the reported results based on attributes which have been selected from different combinations. Conclusion: Applying feature selection on training data before realizing the classification task seems to be a reasonable data-mining process when working with types of data similar to HIV-1. On HIV-1 data, some feature selection or extraction operations in conjunction with different classifiers have been tested and noteworthy outcomes have been reported. These facts motivate for the work presented in this paper. Software availability: The software is available at http://ozyer.etu.edu.tr/c-fs-svm.rar. The software can be downloaded at esnag.etu.edu.tr/software/hiv_cleavage_site_prediction.rar; you will find a readme file which explains how to set the software in order to work. | en_US |
dc.description.sponsorship | Natural Sciences and Engineering Research Council, CanadaNatural Sciences and Engineering Research Council of Canada (NSERC) | en_US |
dc.description.sponsorship | This research is partially supported by a research discovery grant from the Natural Sciences and Engineering Research Council, Canada. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. No additional external funding received for this study. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Public Library Science | en_US |
dc.relation.ispartof | Plos One | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | [No Keywords] | en_US |
dc.title | A Consistency-Based Feature Selection Method Allied With Linear Svms for Hiv-1 Protease Cleavage Site Prediction | en_US |
dc.type | Article | en_US |
dc.department | Faculties, Faculty of Engineering, Department of Computer Engineering | en_US |
dc.department | Fakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü | tr_TR |
dc.identifier.volume | 8 | en_US |
dc.identifier.issue | 8 | en_US |
dc.identifier.wos | WOS:000324403200001 | en_US |
dc.identifier.scopus | 2-s2.0-84882994745 | en_US |
dc.institutionauthor | Özyer, Tansel | - |
dc.identifier.pmid | 24058397 | en_US |
dc.identifier.doi | 10.1371/journal.pone.0063145 | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.scopusquality | Q1 | - |
item.openairetype | Article | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | 02.1. Department of Artificial Intelligence Engineering | - |
Appears in Collections: | Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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