Selected article for: "ANN artificial neural network method and artificial neural network"
Author: Miyssa I. Abdelmageed; Abdelrahman H. Abdelmoneim; Mujahed I. Mustafa; Nafisa M. Elfadol; Naseem S. Murshed; Shaza W. Shantier; Abdelrafie M. Makhawi
Title: Design of multi epitope-based peptide vaccine against E protein of human COVID-19: An immunoinformatics approach
  • Document date: 2020_2_11
    • ID: 6ojmmmuj_18
    Snippet: IEDB tools were used to predict the conserved sequences (10-mersequence) from HLA class I and class II T-cell epitopes by using artificial neural network (ANN) approach [40] [41] [42] . Artificial Neural Network (ANN) version 2.2 was chosen as Prediction method as it depends on the median inhibitory concentration (IC50) [40, [43] [44] [45] . For the binding analysis, all the alleles were carefully chosen, and the length was set at 10 before predi.....
    Document: IEDB tools were used to predict the conserved sequences (10-mersequence) from HLA class I and class II T-cell epitopes by using artificial neural network (ANN) approach [40] [41] [42] . Artificial Neural Network (ANN) version 2.2 was chosen as Prediction method as it depends on the median inhibitory concentration (IC50) [40, [43] [44] [45] . For the binding analysis, all the alleles were carefully chosen, and the length was set at 10 before prediction was done. Analysis of epitopes binding to MHC class I and II molecules was assessed by the IEDB MHC prediction server at (http://tools.iedb.org/mhci/) and (http://tools.iedb.org/mhcii/), respectively. All conserved immunodominant peptides binding to MHC I and II molecules at score equal or less than 100 median inhibitory concentrations (IC50) and 1000, respectively were selected for further analysis while epitopes with IC50 greater than 100 were eliminated [46] .

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