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dc.contributor.authorJayavelu, Naresh Doni
dc.contributor.authorAasgaard, Lasse Svenkerud
dc.contributor.authorBar, Nadav
dc.date.accessioned2015-11-17T12:32:31Z
dc.date.accessioned2015-11-18T09:28:01Z
dc.date.available2015-11-17T12:32:31Z
dc.date.available2015-11-18T09:28:01Z
dc.date.issued2015
dc.identifier.citationBMC Bioinformatics 2015, 16nb_NO
dc.identifier.issn1471-2105
dc.identifier.urihttp://hdl.handle.net/11250/2364441
dc.description.abstractBackground: Network component analysis (NCA) became a popular tool to understand complex regulatory networks. The method uses high-throughput gene expression data and a priori topology to reconstruct transcription factor activity profiles. Current NCA algorithms are constrained by several conditions posed on the network topology, to guarantee unique reconstruction (termed compliancy). However, the restrictions these conditions pose are not necessarily true from biological perspective and they force network size reduction, pruning potentially important components. Results: To address this, we developed a novel, Iterative Sub-Network Component Analysis (ISNCA) for reconstructing networks at any size. By dividing the initial network into smaller, compliant subnetworks, the algorithm first predicts the reconstruction of each subntework using standard NCA algorithms. It then subtracts from the reconstruction the contribution of the shared components from the other subnetwork. We tested the ISNCA on real, large datasets using various NCA algorithms. The size of the networks we tested and the accuracy of the reconstruction increased significantly. Importantly, FOXA1, ATF2, ATF3 and many other known key regulators in breast cancer could not be incorporated by any NCA algorithm because of the necessary conditions. However, their temporal activities could be reconstructed by our algorithm, and therefore their involvement in breast cancer could be analyzed. Conclusions: Our framework enables reconstruction of large gene expression data networks, without reducing their size or pruning potentially important components, and at the same time rendering the results more biological plausible. Our ISNCA method is not only suitable for prediction of key regulators in cancer studies, but it can be applied to any high-throughput gene expression data.nb_NO
dc.language.isoengnb_NO
dc.publisherBioMed Centralnb_NO
dc.titleIterative sub-network component analysis enables reconstruction of large scale genetic networksnb_NO
dc.typeJournal articlenb_NO
dc.typePeer revieweden_GB
dc.date.updated2015-11-17T12:32:31Z
dc.source.volume16nb_NO
dc.source.journalBMC Bioinformaticsnb_NO
dc.identifier.doi10.1186/s12859-015-0768-9
dc.identifier.cristin1289971
dc.description.localcode© 2015 Jayavelu et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.nb_NO


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