A multilayer perceptron neural network-based approach for the identification of responsiveness to interferon therapy in multiple sclerosis patients

Abstract

Multiple sclerosis is an idiopathic inflammatory disease characterized by multiple focal lesions in the white matter of the central nervous system. Multiple sclerosis patients are usually treated with interferon- β, but disease activity decrease in only 30–40% of patients. In the attempt to differentiate between responders and non-responders, we screened the main genes involved in the interferon signaling pathway for 38 single nucleotide polymorphisms (SNPs) in a multiple sclerosis Caucasian population from South Italy. We then analyzed the data using a multilayer perceptron neural network-based approach, in which we evaluated the global weight of a set of SNPs localized in different genes and their association with response to interferon therapy through a feature selection procedure (a combination of automatic relevance determination and backward elimination). The neural approach appears to be a useful tool in identifying gene polymorphisms involved in the response of patients to interferon therapy: 2 out of 5 genes were identified as containing 4 out of 38 significant single nucleotide polymorphisms, with a global accuracy of 70% in predicting responder and non-responder patients.