{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T06:09:06Z","timestamp":1760249346095},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: The systematic study of subcellular location pattern is very important for fully characterizing the human proteome. Nowadays, with the great advances in automated microscopic imaging, accurate bioimage-based classification methods to predict protein subcellular locations are highly desired. All existing models were constructed on the independent parallel hypothesis, where the cellular component classes are positioned independently in a multi-class classification engine. The important structural information of cellular compartments is missed. To deal with this problem for developing more accurate models, we proposed a novel cell structure-driven classifier construction approach (SC-PSorter) by employing the prior biological structural information in the learning model. Specifically, the structural relationship among the cellular components is reflected by a new codeword matrix under the error correcting output coding framework. Then, we construct multiple SC-PSorter-based classifiers corresponding to the columns of the error correcting output coding codeword matrix using a multi-kernel support vector machine classification approach. Finally, we perform the classifier ensemble by combining those multiple SC-PSorter-based classifiers via majority voting.<\/jats:p>\n               <jats:p>Results: We evaluate our method on a collection of 1636 immunohistochemistry images from the Human Protein Atlas database. The experimental results show that our method achieves an overall accuracy of 89.0%, which is 6.4% higher than the state-of-the-art method.<\/jats:p>\n               <jats:p>Availability and implementation: The dataset and code can be downloaded from https:\/\/github.com\/shaoweinuaa\/.<\/jats:p>\n               <jats:p>Contact: \u00a0dqzhang@nuaa.edu.cn<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btv521","type":"journal-article","created":{"date-parts":[[2015,9,13]],"date-time":"2015-09-13T00:08:24Z","timestamp":1442102904000},"page":"114-121","source":"Crossref","is-referenced-by-count":21,"title":["Human cell structure-driven model construction for predicting protein subcellular location from biological images"],"prefix":"10.1093","volume":"32","author":[{"given":"Wei","family":"Shao","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingxia","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daoqiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2015,9,11]]},"reference":[{"key":"2023020109534287100_btv521-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":"2023020109534287100_btv521-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":"2023020109534287100_btv521-B3","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1038\/nrm3821","article-title":"The emergence of proteome-wide technologies: systematic analysis of proteins comes of age","volume":"15","author":"Breker","year":"2014","journal-title":"Nat. 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