Molecular pathway identification using biological network-regularized logistic models

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dc.citation.journalTitleBMC Genomicsen_US
dc.citation.volumeNumber14(Suppl 8)en_US
dc.contributor.authorZhang, Wenen_US
dc.contributor.authorWan, Ying-wooien_US
dc.contributor.authorAllen, Genevera I.en_US
dc.contributor.authorPang, Kaifangen_US
dc.contributor.authorAnderson, Matthew L.en_US
dc.contributor.authorLiu, Zhandongen_US
dc.date.accessioned2016-03-24T18:24:34Z
dc.date.available2016-03-24T18:24:34Z
dc.date.issued2013en_US
dc.description.abstractBackground: Selecting genes and pathways indicative of disease is a central problem in computational biology. This problem is especially challenging when parsing multi-dimensional genomic data. A number of tools, such as L1-norm based regularization and its extensions elastic net and fused lasso, have been introduced to deal with this challenge. However, these approaches tend to ignore the vast amount of a priori biological network information curated in the literature. Results: We propose the use of graph Laplacian regularized logistic regression to integrate biological networks into disease classification and pathway association problems. Simulation studies demonstrate that the performance of the proposed algorithm is superior to elastic net and lasso analyses. Utility of this algorithm is also validated by its ability to reliably differentiate breast cancer subtypes using a large breast cancer dataset recently generated by the Cancer Genome Atlas (TCGA) consortium. Many of the protein-protein interaction modules identified by our approach are further supported by evidence published in the literature. Source code of the proposed algorithm is freely available at http://www.github.com/zhandong/Logit-Lapnet. Conclusion: Logistic regression with graph Laplacian regularization is an effective algorithm for identifying key pathways and modules associated with disease subtypes. With the rapid expansion of our knowledge of biological regulatory networks, this approach will become more accurate and increasingly useful for mining transcriptomic, epi-genomic, and other types of genome wide association studies.en_US
dc.identifier.citationZhang, Wen, Wan, Ying-wooi, Allen, Genevera I., et al.. "Molecular pathway identification using biological network-regularized logistic models." <i>BMC Genomics,</i> 14(Suppl 8), (2013) BioMed Central: http://dx.doi.org/10.1186/1471-2164-14-S8-S7.
dc.identifier.doihttp://dx.doi.org/10.1186/1471-2164-14-S8-S7en_US
dc.identifier.urihttps://hdl.handle.net/1911/88640
dc.language.isoengen_US
dc.publisherBioMed Central
dc.rightsThis article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://​creativecommons.​org/​licenses/​by/​2.​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.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/2.0/en_US
dc.titleMolecular pathway identification using biological network-regularized logistic modelsen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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