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

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 [email protected], 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.

[1]  S. Gabriel,et al.  The Structure of Haplotype Blocks in the Human Genome , 2002, Science.

[2]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[3]  D. Silberberg,et al.  New diagnostic criteria for multiple sclerosis: Guidelines for research protocols , 1983, Annals of neurology.

[4]  J. Listgarten,et al.  Predictive Models for Breast Cancer Susceptibility from Multiple Single Nucleotide Polymorphisms , 2004, Clinical Cancer Research.

[5]  T. Olsson,et al.  Lack of association between the interferon regulatory factor-1 (IRF1) locus at 5q31.1 and multiple sclerosis in Germany, Northern Italy, Sardinia and Sweden , 2000, Genes and Immunity.

[6]  Fuencisla Matesanz,et al.  IFNAR1 and IFNAR2 polymorphisms confer susceptibility to multiple sclerosis but not to interferon-beta treatment response , 2005, Journal of Neuroimmunology.

[7]  Murali Ramanathan,et al.  Genomic Effects of IFN-β in Multiple Sclerosis Patients 1 , 2003, The Journal of Immunology.

[8]  Michael Hutchinson,et al.  Pharmacogenomics of responsiveness to interferon IFN‐β treatment in multiple sclerosis: A genetic screen of 100 type I interferon‐inducible genes , 2005, Clinical pharmacology and therapeutics.

[9]  Eytan Domany,et al.  Models of Neural Networks I , 1991 .

[10]  N. Saitou,et al.  Synonymous mutations in the human dopamine receptor D2 (DRD2) affect mRNA stability and synthesis of the receptor. , 2003, Human molecular genetics.

[11]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[12]  D. Li,et al.  Interferon beta‐1b is effective in relapsing‐remitting multiple sclerosis , 1993, Neurology.

[13]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[14]  G. Ebers,et al.  Randomised double-blind placebo-controlled study of interferon β-1a in relapsing/remitting multiple sclerosis , 1998, The Lancet.

[15]  Michael E. Tipping.  Bayesian Inference: An Introduction to Principles and Practice in Machine Learning , 2003, Advanced Lectures on Machine Learning.

[16]  Hans Henrik Thodberg,et al.  A review of Bayesian neural networks with an application to near infrared spectroscopy , 1996, IEEE Trans. Neural Networks.

[17]  C. Granger,et al.  Intramuscular interferon beta‐1a for disease progression in relapsing multiple sclerosis , 1996, Annals of neurology.

[18]  L. Greller,et al.  Transcription-Based Prediction of Response to IFNβ Using Supervised Computational Methods , 2004, PLoS biology.

[19]  Antony Browne,et al.  Automatic Relevance Determination for Identifying Thalamic Regions Implicated in Schizophrenia , 2008, IEEE Transactions on Neural Networks.

[20]  Geoffrey E. Hinton,et al.  Bayesian Learning for Neural Networks , 1995 .

[21]  David G. Stork,et al.  Pattern Classification , 1973 .

[22]  P. Duquette,et al.  Interferon beta-1b is effective in relapsing-remitting multiple sclerosis. I. Clinical results of a multicenter, randomized, double-blind, placebo-controlled trial. The IFNB Multiple Sclerosis Study Group. , 1993 .

[23]  Ho-Youl Jung,et al.  New methods for imputation of missing genotype using linkage disequilibrium and haplotype information , 2007, Inf. Sci..

[24]  Hiroyuki Honda,et al.  Artificial neural network approach for selection of susceptible single nucleotide polymorphisms and construction of prediction model on childhood allergic asthma , 2004, BMC Bioinformatics.

[25]  I. Ulusoy,et al.  Automatic Relevance Determination for the Estimation of Relevant Features for Object Recognition , 2006, 2006 IEEE 14th Signal Processing and Communications Applications.

[26]  Pablo Villoslada,et al.  Genome-wide pharmacogenomic analysis of the response to interferon beta therapy in multiple sclerosis. , 2008, Archives of neurology.

[27]  George Stark,et al.  Isolation and Characterization of a Human STAT1Gene Regulatory Element , 2002, The Journal of Biological Chemistry.

[28]  Sankar K. Pal,et al.  Soft data mining, computational theory of perceptions, and rough-fuzzy approach , 2004, Inf. Sci..

[29]  Arne Svejgaard,et al.  The immunogenetics of multiple sclerosis , 2008, Immunogenetics.

[30]  T. Decker,et al.  IFNs and STATs in innate immunity to microorganisms. , 2002, The Journal of clinical investigation.

[31]  Ian T. Nabney,et al.  Netlab: Algorithms for Pattern Recognition , 2002 .

[32]  David J. C. MacKay,et al.  Bayesian Methods for Backpropagation Networks , 1996 .

[33]  X. Montalban,et al.  Pharmacogenomic analysis of interferon receptor polymorphisms in multiple sclerosis , 2003, Genes and Immunity.

[34]  P. Rieckmann,et al.  Basic and escalating immunomodulatory treatments in multiple sclerosis: Current therapeutic recommendations , 2008, Journal of Neurology.

[35]  Gunnar Rätsch,et al.  Advanced Lectures on Machine Learning , 2004, Lecture Notes in Computer Science.

[36]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[37]  Elena Salvatore,et al.  Multiple sclerosis and hepatitis C virus infection are associated with single nucleotide polymorphisms in interferon pathway genes. , 2008, Journal of interferon & cytokine research : the official journal of the International Society for Interferon and Cytokine Research.

[38]  Roseane Maia Santos.  Genomic effects of interferon-beta in multiple sclerosis patients , 2005 .

[39]  Per Unneberg,et al.  SNP discovery using advanced algorithms and neural networks , 2005, Bioinform..