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dc.contributor.authorDitzler, Gregory
dc.contributor.authorBouaynaya, Nidhal
dc.contributor.authorShterenberg, Roman
dc.contributor.authorFathallah-Shaykh, Hassan M
dc.date.accessioned2019-06-17T18:34:29Z
dc.date.available2019-06-17T18:34:29Z
dc.date.issued2019-02-06
dc.identifier.citationDitzler, G., Bouaynaya, N., Shterenberg, R., & Fathallah-Shaykh, H. M. (2019). Approximate kernel reconstruction for time-varying networks. BioData mining, 12(1), 5.en_US
dc.identifier.issn1756-0381
dc.identifier.pmid30774716
dc.identifier.doi10.1186/s13040-019-0192-1
dc.identifier.urihttp://hdl.handle.net/10150/632914
dc.description.abstractTo address the problem of inferring sparse time-varying networks from a set of under-sampled measurements, we propose the Approximate Kernel RecONstruction (AKRON) Kalman filter. AKRON supersedes the Lasso regularization by starting from the Lasso-Kalman inferred network and judiciously searching the space for a sparser solution. We derive theoretical bounds for the optimality of AKRON. We evaluate our approach against the Lasso-Kalman filter on synthetic data. The results show that not only does AKRON-Kalman provide better reconstruction errors, but it is also better at identifying if edges exist within a network. Furthermore, we perform a real-world benchmark on the lifecycle (embryonic, larval, pupal, and adult stages) of the Drosophila Melanogaster.en_US
dc.description.sponsorshipNational Science Foundation (NSF) [CCF-1527822, DUE-1610911]en_US
dc.language.isoenen_US
dc.publisherBMCen_US
dc.relation.urlhttps://biodatamining.biomedcentral.com/articles/10.1186/s13040-019-0192-1en_US
dc.rights© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License.en_US
dc.subjectCompressive sensingen_US
dc.subjectGene regulatoryen_US
dc.subjectGene regulatory networksen_US
dc.subjectTime-varying networken_US
dc.titleApproximate kernel reconstruction for time-varying networksen_US
dc.typeArticleen_US
dc.contributor.departmentUniv Arizona, Dept Elect & Comp Engnen_US
dc.identifier.journalBIODATA MININGen_US
dc.description.noteOpen access journalen_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal published versionen_US
dc.source.journaltitleBioData mining
refterms.dateFOA2019-06-17T18:34:30Z


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