Discriminative local subspaces in gene expression data for effective gene function prediction

dc.date.accessioned2020-03-11T20:37:55Z
dc.date.accessioned2022-10-18T23:07:09Z
dc.date.available2020-03-11T20:37:55Z
dc.date.available2022-10-18T23:07:09Z
dc.date.created2020-03-11T20:37:55Z
dc.date.issued2012
dc.identifierhttp://hdl.handle.net/10533/241447
dc.identifier15090007
dc.identifierWOS:000308019200006
dc.identifierno scielo
dc.identifiereid=2-s2.0-84865569505
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/4472786
dc.description.abstractMOTIVATION:Massive amounts of genome-wide gene expression data have become available, motivating the development of computational approaches that leverage this information to predict gene function. Among successful approaches, supervised machine learning
dc.languageeng
dc.relationhttps://doi.org/10.1093/bioinformatics/bts455
dc.relation10.1093/bioinformatics/bts455
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/
dc.titleDiscriminative local subspaces in gene expression data for effective gene function prediction
dc.typeArticulo


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