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dc.contributor.authorAgrahari, Rupesh ( )
dc.contributor.authorForoushani, Amir ( Orcid Icon 0000-0003-2748-3009 )
dc.contributor.authorDocking, T. Roderick ( Orcid Icon http://orcid.org/0000-0003-3248-4081 )
dc.contributor.authorChang, Linda ( )
dc.contributor.authorDuns, Gerben ( )
dc.contributor.authorHudoba, Monika ( )
dc.contributor.authorKarsan, Aly ( )
dc.contributor.authorZare, Habil ( Orcid Icon 0000-0001-5902-6238 )
dc.date.accessioned2019-08-27T13:00:49Z
dc.date.available2019-08-27T13:00:49Z
dc.date.issued2018-05
dc.identifier.citationAgrahari, R., Foroushani, A., Docking, T. R., Chang, L., Duns, G., Hudoba, M., Karsan, A., & Zare, H. (2018). Applications of Bayesian network models in predicting types of hematological malignancies. Scientific Reports, 8(6951).en_US
dc.identifier.urihttps://digital.library.txstate.edu/handle/10877/8546
dc.description.abstractNetwork analysis is the preferred approach for the detection of subtle but coordinated changes in expression of an interacting and related set of genes. We introduce a novel method based on the analyses of coexpression networks and Bayesian networks, and we use this new method to classify two types of hematological malignancies; namely, acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). Our classifier has an accuracy of 93%, a precision of 98%, and a recall of 90% on the training dataset (n = 366); which outperforms the results reported by other scholars on the same dataset. Although our training dataset consists of microarray data, our model has a remarkable performance on the RNA-Seq test dataset (n = 74, accuracy = 89%, precision = 88%, recall = 98%), which confirms that eigengenes are robust with respect to expression profiling technology. These signatures are useful in classification and correctly predicting the diagnosis. They might also provide valuable information about the underlying biology of diseases. Our network analysis approach is generalizable and can be useful for classifying other diseases based on gene expression profiles. Our previously published Pigengene package is publicly available through Bioconductor, which can be used to conveniently fit a Bayesian network to gene expression data.en_US
dc.formatText
dc.format.extent12 pages
dc.format.medium1 file (.pdf)
dc.language.isoen_USen_US
dc.publisherNature Publishing Groupen_US
dc.sourceScientific Reports, 2018, Vol. 8, Article 6951
dc.subjectBayesian network models
dc.subjectHematological malignancies
dc.subjectGenes
dc.titleApplications of Bayesian Network Models in Predicting Types of Hematological Malignanciesen_US
txstate.documenttypeArticle
dc.identifier.doihttps://doi.org/10.1038/s41598-018-24758-5
txstate.departmentComputer Science


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