Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/129612
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dc.contributor.authorDevika Subramanian
dc.date.accessioned2017-04-30T13:30:19Z-
dc.date.available2017-04-30T13:30:19Z-
dc.date.issued2007
dc.identifier.urihttp://hdl.handle.net/123456789/129612-
dc.description.abstractThe course is the second module of a three module course entitled "Bioinformatics: from sequence to structure". This course focuses on learning statistical models from biological data. Three problems are covered: gene finding, classification of gene expression data, and inferring regulatory networks from mRNA and proteomic data. The computational techniques covered include: HMMs, support vector machines, and structure learning with Bayesian networks. This course is made possible by a curriculum development grant from the NSF.
dc.language.isoeng
dc.publisherRice University
dc.relation.urihttp://cnx.org/contents/041eb1d5-6512-489d-81b0-847a8928ab11@2.1/Statistical_machine_learning_f
dc.rights.uriCreative Commons Attribution License (by 2.0)
dc.sourceopenstax cnx
dc.subject.classificationMathematics and StatisticsScience and Technology
dc.subject.otherbayesian networks
dc.subject.otherclassification of microarray data
dc.subject.othercomputational gene finding
dc.subject.otherHidden Markov models
dc.subject.otherlearning regulatory networks from microarray and proteomic data
dc.subject.otherstasistical machine learning
dc.subject.othersupport vector machines
dc.titleStatistical machine learning for computational biology
dc.type電子教課書
dc.classification自然科學類
Theme:教科書-自然科學類

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