| Summary: | 博士 === 國立中央大學 === 資訊工程研究所 === 98 === There are many bioinformatic methods for predicting protein’s functions. In this dissertation, we show how to apply graph theory to a protein-protein interaction network to predict essential proteins and functional modules. Based on the neighborhood of an essential protein is usually larger and denser than that of a non-essential protein, we proposal three methods to predict essential proteins. We also design a double screening scheme, which combines the results computed by two different methods, to generate a superior result. For predicting functional modules, we develop a clustering method which not only extract functional modules from a weighted PPI network, but also use gene expression data as optional input to increase the quality of outcomes. We also propose a measure to judge a cluster and use this measure to develop a framework that integrates the different clustering results to produce a better result.
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