Protein Function Prediction Based On Protein Network Functional Modules
Keywords
Protein Interaction Network, Protein Function Prediction, Clustering
Abstract
In the post-genomic era when gene sequences have been deciphered, with the rapid development of systems biology experiments, a large amount of protein interaction data has been generated. Using these data to find functional modules and predict protein functions is of great significance in functional genomic research. Breaking The traditional clustering model based on the similarity between proteins starts directly from the perspective of protein functional groups and considers the first-order and second-order interactions between functional groups. The modular clustering method (mcm) is proposed to cluster experimental data. Class analysis is used to predict the function of unknown proteins within the module. The prediction ability analysis and stability analysis of the clustering results are carried out through the hypergeometric distribution p-value method and the addition, deletion and modification interaction method. The results show that the modular clustering method It has high prediction accuracy and coverage, and has good fault tolerance and stability. In addition, modular clustering analysis has obtained the prediction results of some unknown proteins with high prediction accuracy, which will provide guidance for biological experiments. meaning, its algorithm also has general significance for other networks with similar structures.
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Original research done by Lu Hongchao, Shi Qiuyan, Shi Baochen, Zhang Zhihua, Zhao Yi, Tang Suqin, Xiong Lei, Wang Qiang, Chen Runsheng
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