Protein Function Prediction using Protein Interaction Networks

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Babapour Khosravi, Niloufar | 2008

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 39346 (05)
University: Sharif University of Technology
Department: Electrical Engineering
Advisor(s): Fatemizadeh, Emadoddin
Abstract:
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 different functions to reduce calculation time, and takes account of topological structure information by a solving a circuit model of protein interaction network to achieve a more accurate prediction. The proposed method outperforms previous approaches and improves the accuracy of protein function prediction for all proteins up to 75%. Particularly, for proteins with small number of neighbors this improvement is up to 78%
Keywords:
Bioinformatics ; Physical interactions ; Protein Interaction Network ; Protein Function Prediction

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