Reordering based integrative expression profiling for microarray classification

dc.contributor.authorWu, Xiaogang
dc.contributor.authorHuang, Hui
dc.contributor.authorSonachalam, Madhankumar
dc.contributor.authorReinhard, Sina
dc.contributor.authorShen, Jeffrey
dc.contributor.authorPandey, Ragini
dc.contributor.authorChen, Jake Y.
dc.contributor.departmentBiomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
dc.date.accessioned2025-07-07T09:20:34Z
dc.date.available2025-07-07T09:20:34Z
dc.date.issued2012-03-13
dc.description.abstractBackground: Current network-based microarray analysis uses the information of interactions among concerned genes/gene products, but still considers each gene expression individually. We propose an organized knowledge-supervised approach - Integrative eXpression Profiling (IXP), to improve microarray classification accuracy, and help discover groups of genes that have been too weak to detect individually by traditional ways. To implement IXP, ant colony optimization reordering (ACOR) algorithm is used to group functionally related genes in an ordered way. Results: Using Alzheimer's disease (AD) as an example, we demonstrate how to apply ACOR-based IXP approach into microarray classifications. Using a microarray dataset - GSE1297 with 31 samples as training set, the result for the blinded classification on another microarray dataset - GSE5281 with 151 samples, shows that our approach can improve accuracy from 74.83% to 82.78%. A recently-published 1372-probe signature for AD can only achieve 61.59% accuracy in the same condition. The ACOR-based IXP approach also has better performance than the IXP approach based on classic network ranking, graph clustering, and random-ordering methods in an overall classification performance comparison. Conclusions: The ACOR-based IXP approach can serve as a knowledge-supervised feature transformation approach to increase classification accuracy dramatically, by transforming each gene expression profile to an integrated expression files as features inputting into standard classifiers. The IXP approach integrates both gene expression information and organized knowledge - disease gene/protein network topology information, which is represented as both network node weights (local topological properties) and network node orders (global topological characteristics).
dc.eprint.versionFinal published version
dc.identifier.citationWu X, Huang H, Sonachalam M, et al. Reordering based integrative expression profiling for microarray classification. BMC Bioinformatics. 2012;13 Suppl 2(Suppl 2):S1. Published 2012 Mar 13. doi:10.1186/1471-2105-13-S2-S1
dc.identifier.urihttps://hdl.handle.net/1805/49192
dc.language.isoen_US
dc.publisherSpringer Nature
dc.relation.isversionof10.1186/1471-2105-13-S2-S1
dc.relation.journalBMC Bioinformatics
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourcePMC
dc.subjectAlzheimer disease
dc.subjectGene expression profiling
dc.subjectOligonucleotide array sequence analysis
dc.subjectCluster analysis
dc.titleReordering based integrative expression profiling for microarray classification
dc.typeArticle
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