Learning representation for multiple biological networks via a robust graph regularized integration approach

Brief Bioinform. 2021 Oct 5:bbab409. doi: 10.1093/bib/bbab409. Online ahead of print.ABSTRACTLearning node representation is a fundamental problem in biological network analysis, as compact representation features reveal complicated network structures and carry useful information for downstream tasks such as link prediction and node classification. Recently, multiple networks that profile objects from different aspects are increasingly accumulated, providing the opportunity to learn objects from multiple perspectives. However, the complex common and specific information across different networks pose challenges to node representation methods. Moreover, ubiquitous noise in networks calls for more robust representation. To deal with these problems, we present a representation learning method for multiple biological networks. First, we accommodate the noise and spurious edges in networks using denoised diffusion, providing robust connectivity structures for the subsequent representation learning. Then, we introduce a graph regularized integration model to combine refined networks and compute common representation features. By using the regularized decomposition technique, the proposed model can effectively preserve the common structural property of different networks and simultaneously accommodate their specific information, leading to a consistent representation. A simulation study shows the superiority of the proposed method on different levels of noisy networks. Three network...

Source: Briefings in Bioinformatics - October 4, 2021 Category: Bioinformatics Authors: Xiwen Zhang Weiwen Wang Chuan-Xian Ren Dao-Qing Dai Source Type: research