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dc.contributor.authorChen, Yi-Hsiungen_US
dc.contributor.authorYang, Chi-Dungen_US
dc.contributor.authorTseng, Ching-Pingen_US
dc.contributor.authorHuang, Hsien-Daen_US
dc.contributor.authorHo, Shinn-Yingen_US
dc.date.accessioned2015-12-02T02:59:08Z-
dc.date.available2015-12-02T02:59:08Z-
dc.date.issued2015-07-01en_US
dc.identifier.issn1367-4803en_US
dc.identifier.urihttp://dx.doi.org/10.1093/bioinformatics/btv075en_US
dc.identifier.urihttp://hdl.handle.net/11536/127861-
dc.description.abstractMotivation: The establishment of quantitative gene regulatory networks (qGRNs) through existing network component analysis (NCA) approaches suffers from shortcomings such as usage limitations of problem constraints and the instability of inferred qGRNs. The proposed GeNOSA framework uses a global optimization algorithm (OptNCA) to cope with the stringent limitations of NCA approaches in large-scale qGRNs. Results: OptNCA performs well against existing NCA-derived algorithms in terms of utilization of connectivity information and reconstruction accuracy of inferred GRNs using synthetic and real Escherichia coli datasets. For comparisons with other non-NCA-derived algorithms, OptNCA without using known qualitative regulations is also evaluated in terms of qualitative assessments using a synthetic Saccharomyces cerevisiae dataset of the DREAM3 challenges. We successfully demonstrate GeNOSA in several applications including deducing condition-dependent regulations, establishing high-consensus qGRNs and validating a sub-network experimentally for dose-response and time-course microarray data, and discovering and experimentally confirming a novel regulation of CRP on AscG.en_US
dc.language.isoen_USen_US
dc.titleGeNOSA: inferring and experimentally supporting quantitative gene regulatory networks in prokaryotesen_US
dc.typeArticleen_US
dc.identifier.doi10.1093/bioinformatics/btv075en_US
dc.identifier.journalBIOINFORMATICSen_US
dc.citation.volume31en_US
dc.citation.issue13en_US
dc.citation.spage2151en_US
dc.citation.epage2158en_US
dc.contributor.department生物科技學系zh_TW
dc.contributor.department生物資訊及系統生物研究所zh_TW
dc.contributor.departmentDepartment of Biological Science and Technologyen_US
dc.contributor.departmentInstitude of Bioinformatics and Systems Biologyen_US
dc.identifier.wosnumberWOS:000357425800012en_US
dc.citation.woscount0en_US
Appears in Collections:Articles