Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

Classification of dengue fever patients based on gene expression data using support vector machines

Gomes, ALV and Wee, LJK and Khan, AM and Gil, LHVG and Marques, ETA and Calzavara-Silva, CE and Tan, TW (2010) Classification of dengue fever patients based on gene expression data using support vector machines. PLoS ONE, 5 (6).

[img]
Preview
PDF
Published Version
Available under License : See the attached license file.

Download (282kB) | Preview
[img] Plain Text (licence)
Available under License : See the attached license file.

Download (1kB)

Abstract

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. © 2010 Gomes et al.


Share

Citation/Export:
Social Networking:
Share |

Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Gomes, ALV
Wee, LJK
Khan, AM
Gil, LHVG
Marques, ETAmarques@pitt.eduMARQUES0000-0003-3826-9358
Calzavara-Silva, CE
Tan, TW
Contributors:
ContributionContributors NameEmailPitt UsernameORCID
EditorBrandstaetter, AnitaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Centers: Other Centers, Institutes, Offices, or Units > Center for Vaccine Research
Date: 11 August 2010
Date Type: Publication
Journal or Publication Title: PLoS ONE
Volume: 5
Number: 6
DOI or Unique Handle: 10.1371/journal.pone.0011267
Schools and Programs: Graduate School of Public Health > Infectious Diseases and Microbiology
Refereed: Yes
MeSH Headings: Dengue--classification; Dengue--genetics; Dengue--immunology; Gene Expression; Humans; Immunity, Innate--genetics; RNA, Messenger--genetics
Other ID: NLM PMC2890409
PubMed Central ID: PMC2890409
PubMed ID: 20585645
Date Deposited: 03 Aug 2012 20:58
Last Modified: 24 Apr 2019 15:55
URI: http://d-scholarship.pitt.edu/id/eprint/13384

Metrics

Monthly Views for the past 3 years

Plum Analytics

Altmetric.com


Actions (login required)

View Item View Item