Classification of Dengue Fever Patients Based on Gene Expression Data Using Support Vector Machines

dc.creatorGomes, Ana Lisa V.
dc.creatorWee, Lawrence J. K.
dc.creatorKhan, Asif M.
dc.creatorGil, Laura H. V. G.
dc.creatorMarques Júnior, Ernesto Torres de Azevedo
dc.creatorSilva, Carlos Eduardo Calzavara
dc.creatorTan, Tin Wee
dc.date2012-09-28T17:13:01Z
dc.date2012-09-28T17:13:01Z
dc.date2010
dc.date.accessioned2023-09-26T21:06:12Z
dc.date.available2023-09-26T21:06:12Z
dc.identifierGOMES, Ana Lisa V. et al. Classification of Dengue Fever Patients Based on Gene Expression Data Using Support Vector Machines. PLoS ONE, v. 5, n. 6, p. 1-7, 2010.
dc.identifier19326203
dc.identifierhttps://www.arca.fiocruz.br/handle/icict/5616
dc.identifier10.1371/journal.pone.0011267
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8869187
dc.descriptionBackground: Symptomatic infection by dengue virus (DENV) can range from dengue fever (DF) to dengue haemorrhagic fever (DHF), however, the determinants of DF or DHF progression are not completely understood. It is hypothesised that host innate immune response factors are involved in modulating the disease outcome and the expression levels of genes involved in this response could be used as early prognostic markers for disease severity. Methodology/Principal Findings: mRNA expression levels of genes involved in DENV innate immune responses were measured using quantitative real time PCR (qPCR). Here, we present a novel application of the support vector machines (SVM) algorithm to analyze the expression pattern of 12 genes in peripheral blood mononuclear cells (PBMCs) of 28 dengue patients (13 DHF and 15 DF) during acute viral infection. The SVM model was trained using gene expression data of these genes and achieved the highest accuracy of ,85% with leave-one-out cross-validation. Through selective removal of gene expression data from the SVM model, we have identified seven genes (MYD88, TLR7, TLR3, MDA5, IRF3, IFN-a and CLEC5A) that may be central in differentiating DF patients from DHF, with MYD88 and TLR7 observed to be the most important. Though the individual removal of expression data of five other genes had no impact on the overall accuracy, a significant combined role was observed when the SVM model of the two main genes (MYD88 and TLR7) was re-trained to include the five genes, increasing the overall accuracy to ,96%. Conclusions/Significance: Here, we present a novel use of the SVM algorithm to classify DF and DHF patients, as well as to elucidate the significance of the various genes involved. It was observed that seven genes are critical in classifying DF and DHF patients: TLR3, MDA5, IRF3, IFN-a, CLEC5A, and the two most important MYD88 and TLR7. While these preliminary results are promising, further experimental investigation is necessary to validate their specific roles in dengue disease.
dc.formatapplication/pdf
dc.languageeng
dc.publisherPublic Library of Science
dc.relationGomes ALV, Wee LJK, Khan AM, Gil LHVG, Marques ETA Jr, et al. (2010) Classification of Dengue Fever Patients Based on Gene Expression Data Using Support Vector Machines. PLoS ONE 5(6): e11267.
dc.rightsopen access
dc.subjectDengue
dc.titleClassification of Dengue Fever Patients Based on Gene Expression Data Using Support Vector Machines
dc.typeArticle


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