Microarray analysis of autoimmune diseases by machine learning procedures

Authors:
Rubén Armañanzas;Borja Calvo;Iñaki Inza;Marcos López-Hoyos;Víctor Martínez-Taboada;Eduardo Ucar;Irantzu Bernales;Asier Fullaondo;Pedro Larrañaga;Ana M. Zubiaga
Affiliations:
Department of Computer Science and Artificial Intelligence, University of the Basque Country, San Sebastian, Spain;Department of Computer Science and Artificial Intelligence, University of the Basque Country, San Sebastian, Spain;Department of Computer Science and Artificial Intelligence, University of the Basque Country, San Sebastian, Spain;Marqués de Valdecilla Hospital, Santander, Spain;Marqués de Valdecilla Hospital, Santander, Spain;Basurto Hospital, Bilbao, Spain;Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country, Bilbao, Spain;Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country, Bilbao, Spain;Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Madrid, Spain;Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country, Bilbao, Spain
Venue:
IEEE Transactions on Information Technology in Biomedicine
Year:
2009

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Abstract

Microarray-based global gene expression profiling, with the use of sophisticated statistical algorithms is providing new insights into the pathogenesis of autoimmune diseases. We have applied a novel statistical technique for gene selection based on machine learning approaches to analyze microarray expression data gathered from patients with systemic lupus erythematosus (SLE) and primary antiphospholipid syndrome (PAPS), two autoimmune diseases of unknown genetic origin that share many common features. The methodology included a combination of three data discretization policies, a consensus gene selection method, and a multivariate correlation measurement. A set of 150 genes was found to discriminate SLE and PAPS patients from healthy individuals. Statistical validations demonstrate the relevance of this gene set from an univariate and multivariate perspective. Moreover, functional characterization of these genes identified an interferon-regulated gene signature, consistent with previous reports. It also revealed the existence of other regulatory pathways, including those regulated by PTEN, TNF, and BCL-2, which are altered in SLE and PAPS. Remarkably, a significant number of these genes carry E2F binding motifs in their promoters, projecting a role for E2F in the regulation of autoimmunity.