Disease gene classification with metagraph representations

• Main Authors: KIRCALI ATA, Sezin, FANG, Yuan, WU, Min, LI, Xiao-Li, XIAO, Xiaokui
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: Disease protein prediction; Metagraph; Protein representations; Protein-protein interaction; Uniprot keywords; Databases and Information Systems; Medicine and Health Sciences
• Online Access: https://ink.library.smu.edu.sg/sis_research/4068; https://ink.library.smu.edu.sg/context/sis_research/article/5071/viewcontent/Disease_gene_manuscript.pdf
• Institution: Singapore Management University

Protein-protein interaction (PPI) networks play an important role in studying the functional roles of proteins, including their association with diseases. However, protein interaction networks are not sufficient without the support of additional biological knowledge for proteins such as their molecular functions and biological processes. To complement and enrich PPI networks, we propose to exploit biological properties of individual proteins. More specifically, we integrate keywords describing protein properties into the PPI network, and construct a novel PPI-Keywords (PPIK) network consisting of both proteins and keywords as two different types of nodes. As disease proteins tend to have a similar topological characteristics on the PPIK network, we further propose to represent proteins with metagraphs. Different from a traditional network motif or subgraph, a metagraph can capture a particular topological arrangement involving the interactions/ associations between both proteins and keywords. Based on the novel metagraph representations for proteins, we further build classifiers for disease protein classification through supervised learning. Our experiments on three different PPI databases demonstrate that the proposed method consistently improves disease protein prediction across various classifiers, by 15.3% in AUC on average. It outperforms the baselines including the diffusion-based methods (e.g., RWR) and the module-based methods by 13.8–32.9% for overall disease protein prediction. For predicting breast cancer genes, it outperforms RWR, PRINCE and the module-based baselines by 6.6–14.2%. Finally, our predictions also turn out to have better correlations with literature findings from PubMed.