Studies of protein modules in a Protein-Protein Interaction (PPI) network contribute greatly to the understanding of biological mechanisms. With the development of computing science, computational approaches have played an important role in locating protein modules. In this paper, a new approach combining Gene Ontology and amino acid background frequency is introduced to detect the protein modules in the weighted PPI networks. The proposed approach mainly consists of three parts: the feature extraction, the weighted graph construction and the protein complex detection. Firstly, the topology-sequence information is utilized to present the feature of protein complex. Secondly, six types of the weighed graph are constructed by combining PPI network and Gene Ontology information. Lastly, protein complex algorithm is applied to the weighted graph, which locates the clusters based on three conditions, including density, network diameter and the included angle cosine. Experiments have been conducted on two protein complex benchmark sets for yeast and the results show that the approach is more effective compared to five typical algorithms with the performance of f-measure and precision. The combination of protein interaction network with sequence and gene ontology data is helpful to improve the performance and provide a optional method for protein module detection.

This work was published JOURNAL OF THEORETICAL BIOLOGY,2017,412:107-112.titled Combining sequence and Gene Ontology for protein module detection in the Weighted Network