Data mining strategies to improve multiplex microbead immunoassay tolerance in a mouse model of infectious diseases

Akshay Mani, Resmi Ravindran, Soujanya Mannepalli, Daniel Vang, Paul A Luciw, Michael Hogarth, Imran Khan, Viswanathan V Krishnan

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Multiplex methodologies, especially those with high-throughput capabilities generate large volumes of data. Accumulation of such data (e.g., genomics, proteomics, metabolomics etc.) is fast becoming more common and thus requires the development and implementation of effective data mining strategies designed for biological and clinical applications. Multiplex microbead immunoassay (MMIA), on xMAP or MagPix platform (Luminex), which is amenable to automation, offers a major advantage over conventional methods such as Western blot or ELISA, for increasing the efficiencies in serodiagnosis of infectious diseases. MMIA allows detection of antibodies and/or antigens efficiently for a wide range of infectious agents simultaneously in host blood samples, in one reaction vessel. In the process, MMIA generates large volumes of data. In this report we demonstrate the application of data mining tools on how the inherent large volume data can improve the assay tolerance (measured in terms of sensitivity and specificity) by analysis of experimental data accumulated over a span of two years. The combination of prior knowledge with machine learning tools provides an efficient approach to improve the diagnostic power of the assay in a continuous basis. Furthermore, this study provides an in-depth knowledge base to study pathological trends of infectious agents in mouse colonies on a multivariate scale. Data mining techniques using serodetection of infections in mice, developed in this study, can be used as a general model for more complex applications in epidemiology and clinical translational research.

Original languageEnglish (US)
Article numbere0116262
JournalPLoS One
Volume10
Issue number1
DOIs
StatePublished - Jan 23 2015

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Data Mining
immunoassays
Microspheres
Immunoassay
infectious diseases
Communicable Diseases
Data mining
animal models
Assays
Epidemiology
Translational Medical Research
Metabolomics
Knowledge Bases
pathogens
serodiagnosis
artificial intelligence
Automation
mice
metabolomics
Serologic Tests

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

Data mining strategies to improve multiplex microbead immunoassay tolerance in a mouse model of infectious diseases. / Mani, Akshay; Ravindran, Resmi; Mannepalli, Soujanya; Vang, Daniel; Luciw, Paul A; Hogarth, Michael; Khan, Imran; Krishnan, Viswanathan V.

In: PLoS One, Vol. 10, No. 1, e0116262, 23.01.2015.

Research output: Contribution to journalArticle

Mani, Akshay ; Ravindran, Resmi ; Mannepalli, Soujanya ; Vang, Daniel ; Luciw, Paul A ; Hogarth, Michael ; Khan, Imran ; Krishnan, Viswanathan V. / Data mining strategies to improve multiplex microbead immunoassay tolerance in a mouse model of infectious diseases. In: PLoS One. 2015 ; Vol. 10, No. 1.
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