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Including transcription factor information in the superparamagnetic clustering of microarray data

dc.contributor.authorMonsiváis Alonso, María del Pilar
dc.contributor.authorNavarro Muñoz, Jorge Carlos
dc.contributor.authorRiego Ruíz, Lina Raquel
dc.contributor.authorLópez Sandoval, Román
dc.contributor.authorRosu Barbus, Haret-Codratian
dc.contributor.editorElsevier
dc.date.accessioned2018-03-21T23:42:34Z
dc.date.available2018-03-21T23:42:34Z
dc.date.issued2010
dc.identifier.citationM.P. Monsiváis-Alonso, J.C. Navarro-Muñoz, L. Riego-Ruiz, R. López-Sandoval, H.C. Rosu, Including transcription factor information in the superparamagnetic clustering of microarray data, Physica A: Statistical Mechanics and its Applications, Volume 389, Issue 24, 2010, Pages 5689-5697, ISSN 0378-4371, http://dx.doi.org/10.1016/j.physa.2010.09.006.
dc.identifier.urihttp://hdl.handle.net/11627/3517
dc.description.abstract"In this work, we modify the superparamagnetic clustering algorithm (SPC) by adding an extra weight to the interaction formula that considers which genes are regulated by the same transcription factor. With this modified algorithm that we call SPCTF, we analyze Spellman et al. microarray data for cell cycle genes in yeast, and find clusters with a higher number of elements compared with those obtained with the SPC algorithm. Some of the incorporated genes by using SPCFT were not detected at first by Spellman et al. but were later identified by other studies, whereas several genes still remain unclassified. The clusters composed by unidentified genes were analyzed with MUSA, the motif finding using an unsupervised approach algorithm, and this allow us to select the clusters whose elements contain cell cycle transcription factor binding sites as clusters worth of further experimental studies because they would probably lead to new cell cycle genes. Finally, our idea of introducing available infor-mation about transcription factors to optimize the gene classification could be implemented for other distance-based clustering algorithms."
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectSuperparamagnetic clustering
dc.subjectSimilarity measure
dc.subjectMicroarrays
dc.subjectCell cycle genes
dc.subjectTranscription factors
dc.subject.classificationCIENCIAS FíSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA
dc.titleIncluding transcription factor information in the superparamagnetic clustering of microarray data
dc.typearticle
dc.identifier.doihttps://doi.org/10.1016/j.physa.2010.09.006
dc.rights.accessAcceso Abierto


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internacional