Denoising Protein-Protein interaction network via variational graph auto-encoder for protein complex detection.

Journal of Bioinformatics and Computational Biology
Heng YaoTianying Liu

Abstract

Identifying protein complexes is an important issue in computational biology, as it benefits the understanding of cellular functions and the design of drugs. In the past decades, many computational methods have been proposed by mining dense subgraphs in Protein-Protein Interaction Networks (PINs). However, the high rate of false positive/negative interactions in PINs prevents accurately detecting complexes directly from the raw PINs. In this paper, we propose a denoising approach for protein complex detection by using variational graph auto-encoder. First, we embed a PIN to vector space by a stacked graph convolutional network (GCN), then decide which interactions in the PIN are credible. If the probability of an interaction being credible is less than a threshold, we delete the interaction. In such a way, we reconstruct a reliable PIN. Following that, we detect protein complexes in the reconstructed PIN by using several typical detection methods, including CPM, Coach, DPClus, GraphEntropy, IPCA and MCODE, and compare the results with those obtained directly from the original PIN. We conduct the empirical evaluation on four yeast PPI datasets (Gavin, Krogan, DIP and Wiphi) and two human PPI datasets (Reactome and Reactomekb), a...Continue Reading

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Citations

Jul 24, 2020·Journal of Bioinformatics and Computational Biology·Shuigeng Zhou
Aug 17, 2021·Frontiers in Genetics·Xiao-Meng ZhangMing-Jing Tang
Sep 3, 2021·Briefings in Bioinformatics·Hai-Cheng YiChee Keong Kwoh

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