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

BACKGROUND: 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 d...

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Autores principales: Gomes, Ana Lisa V., Wee, Lawrence J. K., Khan, Asif M., Gil, Laura H. V. G., Marques, Ernesto T. A., Calzavara-Silva, Carlos E., Tan, Tin Wee
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2890409/
https://www.ncbi.nlm.nih.gov/pubmed/20585645
http://dx.doi.org/10.1371/journal.pone.0011267
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author Gomes, Ana Lisa V.
Wee, Lawrence J. K.
Khan, Asif M.
Gil, Laura H. V. G.
Marques, Ernesto T. A.
Calzavara-Silva, Carlos E.
Tan, Tin Wee
author_facet Gomes, Ana Lisa V.
Wee, Lawrence J. K.
Khan, Asif M.
Gil, Laura H. V. G.
Marques, Ernesto T. A.
Calzavara-Silva, Carlos E.
Tan, Tin Wee
author_sort Gomes, Ana Lisa V.
collection PubMed
description BACKGROUND: 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-α 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-α, 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.
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spelling pubmed-28904092010-06-28 Classification of Dengue Fever Patients Based on Gene Expression Data Using Support Vector Machines Gomes, Ana Lisa V. Wee, Lawrence J. K. Khan, Asif M. Gil, Laura H. V. G. Marques, Ernesto T. A. Calzavara-Silva, Carlos E. Tan, Tin Wee PLoS One Research Article BACKGROUND: 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-α 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-α, 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. Public Library of Science 2010-06-23 /pmc/articles/PMC2890409/ /pubmed/20585645 http://dx.doi.org/10.1371/journal.pone.0011267 Text en Gomes et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Gomes, Ana Lisa V.
Wee, Lawrence J. K.
Khan, Asif M.
Gil, Laura H. V. G.
Marques, Ernesto T. A.
Calzavara-Silva, Carlos E.
Tan, Tin Wee
Classification of Dengue Fever Patients Based on Gene Expression Data Using Support Vector Machines
title Classification of Dengue Fever Patients Based on Gene Expression Data Using Support Vector Machines
title_full Classification of Dengue Fever Patients Based on Gene Expression Data Using Support Vector Machines
title_fullStr Classification of Dengue Fever Patients Based on Gene Expression Data Using Support Vector Machines
title_full_unstemmed Classification of Dengue Fever Patients Based on Gene Expression Data Using Support Vector Machines
title_short Classification of Dengue Fever Patients Based on Gene Expression Data Using Support Vector Machines
title_sort classification of dengue fever patients based on gene expression data using support vector machines
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2890409/
https://www.ncbi.nlm.nih.gov/pubmed/20585645
http://dx.doi.org/10.1371/journal.pone.0011267
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