Protein functional module identification method combining topological features and gene expression data

摘要

More and more clustering algorithms are proposed to identify protein complexes with the constantly development of proteomics. Although many of those algorithms have been verified to have good performance [1–4], mining the complex only through the protein network itself will inevitably limit the effectiveness of its results, because the available protein data is incomplete due to the diversity of protein network structures and the complexity of data sources, and there is a certain amount of noise in protein networks. Therefore, other biological data such as fusion of gene expression provide new ideas for detecting protein functional modules [5, 6]. For example, Chin et al. [7] proposed method HUNTER to detect functional modules, this method firstly calculates the similarity value of high-throughput data (for example, calculating pairwise similarity of gene expression patterns from microarray data), then, detecting weak signals that cannot be distinguished with existing methods by using the network of genes or proteins and the similarity values between them and by applying network topological constraints to the expression data clusters, finding connected sub-networks (or modules) with highly similarity, which improves the effectiveness of compound identification. Although there are many ways to analyze the network and similar data separately [8–11], there is still a lot of room for development in the method of using two information sources for analysis.