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dc.contributor.authorAlzahrani, Majed A.
dc.contributor.authorKuwahara, Hiroyuki
dc.contributor.authorWang, Wei
dc.contributor.authorGao, Xin
dc.date.accessioned2017-05-17T07:41:39Z
dc.date.available2017-05-17T07:41:39Z
dc.date.issued2017-04-04
dc.identifier.citationAlzahrani M, Kuwahara H, Wang W, Gao X (2017) Gracob: a novel graph-based constant-column biclustering method for mining growth phenotype data. Bioinformatics. Available: http://dx.doi.org/10.1093/bioinformatics/btx199.
dc.identifier.issn1367-4803
dc.identifier.issn1460-2059
dc.identifier.doi10.1093/bioinformatics/btx199
dc.identifier.urihttp://hdl.handle.net/10754/623634
dc.description.abstractGrowth phenotype profiling of genome-wide genedeletion strains over stress conditions can offer a clear picture that the essentiality of genes depends on environmental conditions. Systematically identifying groups of genes from such high-throughput data that share similar patterns of conditional essentiality and dispensability under various environmental conditions can elucidate how genetic interactions of the growth phenotype are regulated in response to the environment.We first demonstrate that detecting such\co-fit
dc.description.sponsorshipThe research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/1976-04. NIH U01HG008488, NIH R01GM115833, NIH U54GM114833, NSF DBI-1565137, and NSF IIS-1313606.
dc.publisherOxford University Press (OUP)
dc.relation.urlhttps://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btx199#80911554
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.titleGracob: a novel graph-based constant-column biclustering method for mining growth phenotype data
dc.typeArticle
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalBioinformatics
dc.eprint.versionPost-print
dc.contributor.institutionDepartment of Computer Science, University of California, Los Angeles, 3531-G Boelter Hall, Los Angeles 90095, CA, USA.
kaust.personAlzahrani, Majed A.
kaust.personKuwahara, Hiroyuki
kaust.personGao, Xin
kaust.grant.numberURF/1/1976-04
kaust.grant.numberNIH U01HG008488
kaust.grant.numberNIH R01GM115833
kaust.grant.numberNIH U54GM114833
kaust.grant.numberNSF DBI-1565137
kaust.grant.numberNSF IIS-1313606
refterms.dateFOA2018-06-13T16:07:33Z
dc.date.published-online2017-04-04
dc.date.published-print2017-08-15


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This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
Except where otherwise noted, this item's license is described as This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com