PPI Inference Algorithms Using MS Data
With the development of proteomics, the focus of research has begun to focus on the establishment of all human protein interaction (PPI) networks. Mass spectrometry has become a representative method for predicting protein interaction. Mass spectrometry is one of the main experimental means to construct protein interaction network. Based on mass spectrometry, a large number of protein purification data, such as AP-MS data and PCP-MS data, have been generated. These data provide important data support for PPI network construction, but it is not only inefficient but also unrealistic to construct PPI network by manual means. Therefore, the network inference algorithm for PCP-MS data is one of the hot topics in bioinformatics. In this paper, the algorithm of constructing a kind of mainstream mass spectrometry data (PCP-MS data) PPI network is studied. Considering the existing bottlenecks, the goal of constructing a high-quality PPI network is achieved. The existing PPI network inference algorithm for PCP-MS data is still in its infancy, and there are few related methods. At the same time, there are some problems in the quality of the results of the algorithm, such as: (1) many wrong interactions are included in the results of different inference algorithms, while some correct interactions are omitted; (2) different inference algorithms perform differently on the same data set; (3) for different data sets, the volatility variance of the performance of the same algorithm is large.
KeywordsMS data PPI network Direct protein interaction Correlation analysis Sequencing integration
This work was supported by grants from The National Natural Science Foundation of China (No. 61862056), the Guangxi Natural Science Foundation (No. 2017GXNSFAA198148) foundation of Wuzhou University (No. 2017B001), Guangxi Colleges and Universities Key Laboratory of Professional Software Technology, Wuzhou University.
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