Gracob: a novel graph-based constant-column biclustering method for mining growth phenotype data

Handle URI:
http://hdl.handle.net/10754/623634
Title:
Gracob: a novel graph-based constant-column biclustering method for mining growth phenotype data
Authors:
Alzahrani, Majed A.; Kuwahara, Hiroyuki; Wang, Wei; Gao, Xin ( 0000-0002-7108-3574 )
Abstract:
Growth 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
KAUST Department:
Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Alzahrani 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.
Publisher:
Oxford University Press (OUP)
Journal:
Bioinformatics
KAUST Grant Number:
URF/1/1976-04; NIH U01HG008488; NIH R01GM115833; NIH U54GM114833; NSF DBI-1565137; NSF IIS-1313606
Issue Date:
5-Apr-2017
DOI:
10.1093/bioinformatics/btx199
Type:
Article
ISSN:
1367-4803; 1460-2059
Sponsors:
The 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.
Additional Links:
https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btx199#80911554
Appears in Collections:
Articles; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorAlzahrani, Majed A.en
dc.contributor.authorKuwahara, Hiroyukien
dc.contributor.authorWang, Weien
dc.contributor.authorGao, Xinen
dc.date.accessioned2017-05-17T07:41:39Z-
dc.date.available2017-05-17T07:41:39Z-
dc.date.issued2017-04-05en
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.en
dc.identifier.issn1367-4803en
dc.identifier.issn1460-2059en
dc.identifier.doi10.1093/bioinformatics/btx199en
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-fiten
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.en
dc.publisherOxford University Press (OUP)en
dc.relation.urlhttps://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btx199#80911554en
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.comen
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/en
dc.titleGracob: a novel graph-based constant-column biclustering method for mining growth phenotype dataen
dc.typeArticleen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalBioinformaticsen
dc.eprint.versionPost-printen
dc.contributor.institutionDepartment of Computer Science, University of California, Los Angeles, 3531-G Boelter Hall, Los Angeles 90095, CA, USA.en
kaust.authorAlzahrani, Majed A.en
kaust.authorKuwahara, Hiroyukien
kaust.authorGao, Xinen
kaust.grant.numberURF/1/1976-04en
kaust.grant.numberNIH U01HG008488en
kaust.grant.numberNIH R01GM115833en
kaust.grant.numberNIH U54GM114833en
kaust.grant.numberNSF DBI-1565137en
kaust.grant.numberNSF IIS-1313606en
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