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dc.contributor.authorGonzález-Domínguez, Jorge
dc.contributor.authorSchmidt, Bertil
dc.date.accessioned2018-08-14T10:01:39Z
dc.date.available2018-08-14T10:01:39Z
dc.date.issued2015
dc.identifier.citationJorge González-Domínguez, Bertil Schmidt, GPU-accelerated exhaustive search for third-order epistatic interactions in case–control studies, Journal of Computational Science, Volume 8, 2015, Pages 93-100, ISSN 1877-7503, https://doi.org/10.1016/j.jocs.2015.04.001.es_ES
dc.identifier.issn1877-7503
dc.identifier.issn1877-7511
dc.identifier.urihttp://hdl.handle.net/2183/20965
dc.descriptionThis is a post-peer-review, pre-copyedit version of an article published in Journal of Computational Science. The final authenticated version is available online at: https://doi.org/10.1016/j.jocs.2015.04.001es_ES
dc.description.abstract[Abstract] Interest in discovering combinations of genetic markers from case–control studies, such as Genome Wide Association Studies (GWAS), that are strongly associated to diseases has increased in recent years. Detecting epistasis, i.e. interactions among k markers (k ≥ 2), is an important but time consuming operation since statistical computations have to be performed for each k-tuple of measured markers. Efficient exhaustive methods have been proposed for k = 2, but exhaustive third-order analyses are thought to be impractical due to the cubic number of triples to be computed. Thus, most previous approaches apply heuristics to accelerate the analysis by discarding certain triples in advance. Unfortunately, these tools can fail to detect interesting interactions. We present GPU3SNP, a fast GPU-accelerated tool to exhaustively search for interactions among all marker-triples of a given case–control dataset. Our tool is able to analyze an input dataset with tens of thousands of markers in reasonable time thanks to two efficient CUDA kernels and efficient workload distribution techniques. For instance, a dataset consisting of 50,000 markers measured from 1000 individuals can be analyzed in less than 22 h on a single compute node with 4 NVIDIA GTX Titan boards. Source code is available at: http://sourceforge.net/projects/gpu3snp/.es_ES
dc.language.isoenges_ES
dc.publisherElsevier Ltdes_ES
dc.relation.urihttps://doi.org/10.1016/j.jocs.2015.04.001es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectGPUes_ES
dc.subjectCUDAes_ES
dc.subjectEpistasises_ES
dc.subjectGWASes_ES
dc.subjectMutual informationes_ES
dc.titleGPU-accelerated exhaustive search for third-order epistatic interactions in case–control studieses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleJournal of Computational Sciencees_ES
UDC.volume8es_ES
UDC.startPage93es_ES
UDC.endPage100es_ES
dc.identifier.doi10.1016/j.jocs.2015.04.001


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