Predicting protein function accurately is an important issue in the post genomic era. To achieve this goal, several approaches have been proposed deduce the function of unclassified proteins through sequence similarity, co expression profiles, and other information. Among these methods, the Global Optimization Method is an interesting and powerful tool that assigns functions to unclassified proteins based on their positions in a physical interaction network. To boost both the accuracy and speed of global optimization method, a new prediction method, Accurate Global Optimization Method (AGOM), is presented in this thesis, which employs optimal repetition method enhanced with frequency of...