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Articles by W.F. Abd El-Wahed
Total Records ( 2 ) for W.F. Abd El-Wahed
  M.B. Youssef , H.A. Nour El-Din , W.F. Abd EL-Wahed and A.A. Hemeida
  Epitopes prediction plays a vital role in the development of antibodies production and immunodiagnostic tests. This paper is focused on building models for predicting linear B-cell epitopes based on Support Vector Machine (SVM) and subsequence string kernel. The obtained models were tested by 10 fold cross validation method. We applied the obtained models to potato Leaf Roll Virus (PLRV) as a case study using Epitopes Model Applier Software (EMAS) which was developed as an open source software and released under General Public License (GPL) to predict immunogenic peptides suitable for antibodies production. The thirty amino acids peptide which start from position 163 to position 192 got high score and match the previous laboratory studies which make it one of the best candidates to be immunogenic and capable of producing antibodies that cross react with PLRV. The peptide was chemically synthesized and injected into animal (mouse). The obtained antibodies were tested by using TAS-ELISA and Immuno dot-blot assay. The obtained antibodies were positively reacted against PLRV infected potato tissues.
  M.S. Osman , W.F. Abd El-Wahed , Mervat M.K. El Shafei and Hanaa B. Abd El Wahab
  Problem statement: We deal with the bi-level linear programming problem. A bi-level programming problem is formulated for a problem in which two Decision-Makers (DMs) make decisions successively. Approach: In this research we studied and designs a Genetic Algorithm (GA) of Bi-Level Linear Programming Problems (BLPP) by constructing the fitness function of the upper-level programming problems based on the definition of the feasible degree. This GA avoids the use of penalty function to deal with the constraints, by changing the randomly generated initial population into an initial population satisfying the constraints in order to improve the ability of the GA to deal with the constraints. Also we designed software to solve this problem. A comparative study between proposed method and previous methods through numerical results of some examples. Finally, parametric information of the GA was introduced. Results: Results of the study showed that the proposed method is feasible and more efficient to solve (BLPP), also there exist package to solve (BLPP) problem. Conclusion: This GA avoids the use of penalty function to deal with the constraints, by changing the randomly generated initial population into an initial population satisfying the constraints in order to improve the ability of the GA to deal with the constraints.
 
 
 
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