title: Data mining strategies to improve multiplex microbead immunoassay tolerance in a mouse model of infectious diseases.
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.
authors.affiliation: Center for Comparative Medicine, University of California Davis, Davis, California, United States of America.
authors.forename: Akshay
authors.initials: A
authors.lastname: Mani
authors.affiliation: Center for Comparative Medicine, University of California Davis, Davis, California, United States of America.
authors.forename: Resmi
authors.initials: R
authors.lastname: Ravindran
authors.affiliation: Department of Chemistry, California State University, Fresno, California, United States of America.
authors.forename: Soujanya
authors.initials: S
authors.lastname: Mannepalli
authors.affiliation: Center for Comparative Medicine, University of California Davis, Davis, California, United States of America.
authors.forename: Daniel
authors.initials: D
authors.lastname: Vang
authors.affiliation: Center for Comparative Medicine, University of California Davis, Davis, California, United States of America; Department of Pathology and Laboratory Medicine, University of California School of Medicine, Davis, California, United States of America.
authors.forename: Paul A
authors.initials: PA
authors.lastname: Luciw
authors.affiliation: Department of Pathology and Laboratory Medicine, University of California School of Medicine, Davis, California, United States of America.
authors.forename: Michael
authors.initials: M
authors.lastname: Hogarth
authors.affiliation: Center for Comparative Medicine, University of California Davis, Davis, California, United States of America; Department of Pathology and Laboratory Medicine, University of California School of Medicine, Davis, California, United States of America.
authors.forename: Imran H
authors.initials: IH
authors.lastname: Khan
authors.affiliation: Center for Comparative Medicine, University of California Davis, Davis, California, United States of America; Department of Chemistry, California State University, Fresno, California, United States of America; Department of Pathology and Laboratory Medicine, University of California School of Medicine, Davis, California, United States of America.
authors.forename: Viswanathan V
authors.initials: VV
authors.lastname: Krishnan
mesh_terms: D000818:Animals; D000914:Antibodies, Viral / Q000276:immunology; D000956:Antigens, Viral / Q000276:immunology; D003141:Communicable Diseases / Q000175:diagnosis* / Q000276:immunology / Q000821:virology; D057225:Data Mining / Q000379:methods*; D004195:Disease Models, Animal; D006801:Humans; D007118:Immunoassay / Q000379:methods*; D000069550:Machine Learning; D051379:Mice; D008863:Microspheres; D014777:Virus Diseases / Q000175:diagnosis / Q000276:immunology*; D014780:Viruses / Q000276:immunology*
chemical_list: D000914:Antibodies, Viral; D000956:Antigens, Viral