Elnaz Qofrani - Imam Reza International University Mashhad, Iran
Mehrdad Jalali - faculty member of Islamic Azad university, Mashhad branch Mashhad, Iran
Mohamad Reza Kalani - the member of informatic educational group of medical sience of mashhad Mashhad, Iran

Gene selection is one of important research issues in analysis of gene expression data classification. Current methods try to reduce genes by means of statistical calculations and haveused semantic similarity under gene ontology. In this article a technique has been presented based on which in addition toconsidering biological relation among genes, redundant genes by means of hierarchical clustering are omitted and the accuracy of classification increases. The structure and function of this technique have also been explained. The experiments using a single real data set indicate that the proposed technique in addition to selecting fewer genes, have higher accuracy of classification (Loocv), comparing to the technique that is based on semantic similarity

Ontology, Gene Selection, Semantic Similarity,Classification of Gene Expression Data