{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T14:03:43Z","timestamp":1724594623590},"reference-count":24,"publisher":"Oxford University Press (OUP)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,1,1]]},"abstract":"Abstract<\/jats:title>\n Motivation: Subcellular localization of proteins is one of the most significant characteristics of living cells. Prediction of protein subcellular locations is crucial to the understanding of various protein functions. Therefore, an accurate, computationally efficient and reliable prediction system is required.<\/jats:p>\n Results: In this article, the predictions of various Support Vector Machine (SVM) models have been combined through majority voting. The proposed ensemble SVM-SubLoc has achieved the highest success rates of 99.7% using hybrid features of Haralick textures and local binary patterns (HarLBP), 99.4% using hybrid features of Haralick textures and Local Ternary Patterns (HarLTP). In addition, SVM-SubLoc has yielded 99.0% accuracy using only local ternary patterns (LTPs) based features. The dimensionality of HarLBP feature vector is 581 compared with 78 and 52 for HarLTP and LTPs, respectively. Hence, SVM-SubLoc in conjunction with LTPs is fast, sufficiently accurate and simple predictive system. The proposed SVM-SubLoc approach thus provides superior prediction performance using the reduced feature space compared with existing approaches.<\/jats:p>\n Availability: A web server accompanying the proposed prediction scheme is available at http:\/\/111.68.99.218\/SVM-SubLoc<\/jats:p>\n Contact: \u00a0asif@pieas.edu.pk; khan.asifullah@gmail.com<\/jats:p>\n Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btr624","type":"journal-article","created":{"date-parts":[[2011,11,17]],"date-time":"2011-11-17T11:42:06Z","timestamp":1321530126000},"page":"91-97","source":"Crossref","is-referenced-by-count":33,"title":["Protein subcellular localization of fluorescence imagery using spatial and transform domain features"],"prefix":"10.1093","volume":"28","author":[{"given":"Muhammad","family":"Tahir","sequence":"first","affiliation":[{"name":"Department of Computer and Information Sciences, PIEAS, Islamabad, Pakistan"}]},{"given":"Asifullah","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Sciences, PIEAS, Islamabad, Pakistan"}]},{"given":"Abdul","family":"Majid","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Sciences, PIEAS, Islamabad, Pakistan"}]}],"member":"286","published-online":{"date-parts":[[2011,11,15]]},"reference":[{"key":"2023061011552288900_B1","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1002\/(SICI)1097-0320(19981101)33:3<366::AID-CYTO12>3.0.CO;2-R","article-title":"Automated recognition of patterns characteristic of subcellular structures in fluorescence microscopy images","volume":"33","author":"Boland","year":"1998","journal-title":"Cytometry"},{"key":"2023061011552288900_B2","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.1093\/bioinformatics\/17.12.1213","article-title":"A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells","volume":"17","author":"Boland","year":"2001","journal-title":"Bioinformatics"},{"key":"2023061011552288900_B3","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1186\/1471-2105-8-210","article-title":"A multiresolution approach to automated classification of protein subcellular location images","volume":"8","author":"Chebira","year":"2007","journal-title":"BMC Bioinformatics"},{"key":"2023061011552288900_B4","volume-title":"Support Vector Machines for Classification and Regression","author":"Gunn","year":"1998"},{"key":"2023061011552288900_B5","first-page":"67","article-title":"Automated Sub-Cellular Phenotype Classification: An Introduction and Recent Results","volume-title":"The 2006 Workshop on Intelligent Systems for Bioinformatics (WISB 2006).","author":"Hamilton","year":"2006"},{"key":"2023061011552288900_B6","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1186\/1471-2105-8-110","article-title":"Fast automated cell phenotype image classification","volume":"8","author":"Hamilton","year":"2007","journal-title":"BMC Bioinformatics"},{"key":"2023061011552288900_B7","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1109\/PROC.1979.11328","article-title":"Statistical and Structural Approaches to Texture","volume":"67","author":"Haralick","year":"1979","journal-title":"Proceedings of the IEEE"},{"key":"2023061011552288900_B8","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.jtbi.2010.11.017","article-title":"Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition","volume":"271","author":"Hayat","year":"2011","journal-title":"J. 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