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Early diagnosis of systemic lupus erythmatosus using ANN models of dsDNA binding antibody sequence data



Mohamad Hasan Bahari1, Mahmoud Mahmoudi2, Asad Azemi3, Mir Mojtaba Mirsalehi4, Morteza Khademi5*



1Katholieke Universiteit Leuven/ESAT, Leuven, Belgium. 2 Immunology Research Center, Mashhad University of Medical Science, Mashhad, Iran. 3 Penn State University /Engineering Department, Delaware, USA. 4 Ferdowsi University of Mashhad/Electrical Engineering Department, Mashhad, Iran. 5 Ferdowsi University of Mashhad/Electrical Engineering Department, Mashhad, Iran.



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Article Type






received  March 28, 2010; revised April 30, 2010; accepted  June 8, 2010; published online July06, 2010




In this paper a new method based on artificial neural networks (ANN), is introduced for identifying pathogenic antibodies in Systemic Lupus Erythmatosus (SLE). dsDNA binding antibodies have been implicated in the pathogenesis of this autoimmune disease. In order to identify these dsDNA binding antibodies, the protein sequences of 42 dsDNA binding and 608 non-dsDNA binding antibodies were extracted from Kabat database and encoded using a physicochemical property of their amino acids namely Hydrophilicity. Encoded antibodies were used as the training patterns of a general regression neural network (GRNN). Simulation results show that the accuracy of proposed method in recognizing dsDNA binding antibodies is 83.2%. We have also investigated the roles of the light and heavy chains of anti-dsDNA antibodies in binding to DNA. Simulation results concur with the published experimental findings that in binding to DNA, the heavy chain of anti-dsDNA is more important than their light chain. 




Anti-dsDNA; Antibody; General Regression Neural Network (GRNN); Systemic Lupus Erythematosus




Bahari et al, Bioinformation 5(2): 58-61 (2010)


Edited by


P. Kangueane







  Biomedical Informatics


  This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.