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dc.contributor.authorWang, Shu-Lin
dc.contributor.authorSun, Liuchao
dc.contributor.authorFang, Jianwen
dc.date.accessioned2015-05-20T17:18:17Z
dc.date.available2015-05-20T17:18:17Z
dc.date.issued2014-12-03
dc.identifier.citationWang, Shu-Lin, et al. "Molecular cancer classification using a meta-sample-based regularized robust coding method." BMC Bioinformatics 2014, 15(Suppl 15):S2

http://dx.doi.org/10.1186/1471-2105-15-S15-S2
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dc.identifier.urihttp://hdl.handle.net/1808/17818
dc.description.abstractMotivation

Previous studies have demonstrated that machine learning based molecular cancer classification using gene expression profiling (GEP) data is promising for the clinic diagnosis and treatment of cancer. Novel classification methods with high efficiency and prediction accuracy are still needed to deal with high dimensionality and small sample size of typical GEP data. Recently the sparse representation (SR) method has been successfully applied to the cancer classification. Nevertheless, its efficiency needs to be improved when analyzing large-scale GEP data. Results

In this paper we present the meta-sample-based regularized robust coding classification (MRRCC), a novel effective cancer classification technique that combines the idea of meta-sample-based cluster method with regularized robust coding (RRC) method. It assumes that the coding residual and the coding coefficient are respectively independent and identically distributed. Similar to meta-sample-based SR classification (MSRC), MRRCC extracts a set of meta-samples from the training samples, and then encodes a testing sample as the sparse linear combination of these meta-samples. The representation fidelity is measured by the l2-norm or l1-norm of the coding residual. Conclusions

Extensive experiments on publicly available GEP datasets demonstrate that the proposed method is more efficient while its prediction accuracy is equivalent to existing MSRC-based methods and better than other state-of-the-art dimension reduction based methods.
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dc.description.sponsorshipThis article was funded by the National Science Foundation of China on finding tumor-related driver pathway with comprehensive analysis method based on next-generation sequencing data and the dimension reduction of gene expression data based on heuristic method (grant nos. 61474267, 60973153 and 61133010) and the National Institutes of Health (NIH) Grant P01 AG12993 (PI: E. Michaelis).

This article has been published as part of BMC Bioinformatics Volume 15 Supplement 15, 2014: Proceedings of the 2013 International Conference on Intelligent Computing (ICIC 2013). The full contents of the supplement are available online at http://www.biomedcentral.com/bmcbioinformatics/supplements/15/S15.
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dc.publisherBioMedCentralen_US
dc.rights© 2014 Wang et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleMolecular cancer classification using an meta-sample-based regularized robust coding methoden_US
dc.typeArticle
kusw.kuauthorWang, Shu-Lin
kusw.kuauthorFang, Jianwen
kusw.kudepartmentApplied Bioinformatics Laboratoryen_US
dc.identifier.doi10.1186/1471-2105-15-S15-S2
kusw.oaversionScholarly/refereed, publisher version
kusw.oapolicyThis item meets KU Open Access policy criteria.
dc.rights.accessrightsopenAccess


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© 2014 Wang et al.; licensee BioMed Central Ltd.  This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Except where otherwise noted, this item's license is described as: © 2014 Wang et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.