| Summary: | 碩士 === 國立虎尾科技大學 === 光電與材料科技研究所 === 100 === It is known that protein complexes are involved in many biological processes. Most of the protein complex prediction algorithms are based on the assumption that protein-protein interaction (PPI) dense regions can possibly lead to complexes formation. In this study we propose the inclusion of physiochemical properties is necessary for improving protein complex prediction. Principle component analysis is carried out to determine the major features. Cross-validation test is performed to test the classification accuracy of four machine learning methods; i.e. support vector machining, neural network, decision tree and Bayes classifier.
To study the effectiveness of adopting physiochemical properties on protein complex prediction, we shown that prediction accuracy can be improved post-processing prediction methods’ results with an amino acid composition profile. A web service for protein complex prediction has been set up, which can be accessed at http://bioinfo.csie.nfu.edu.tw:8080/ProteinComplex/ProteinComplexsvm.aspx
The question of the robustness of a biological network upon perturbation is an important issue in systems biology study. In this thesis, we extend the idea of protein-protein interaction network by considering interactions among protein complexes, and construct the so-called protein complex networks (PCNs). Stability of two species’ PCNs is studied. Except the attack perturbation, the PCNs of yeast and human species are quite robust with respect to failure, rewiring and edgedeletion perturbations.
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