Disease gene classification with metagraph representations

• Main Authors: KIRCALI ATA, Sezin, FANG, Yuan, WU, Min, LI, Xiao-Li, XIAO, Xiaokui
• 格式: text
• 語言: English
• 出版: Institutional Knowledge at Singapore Management University 2018
• 主題: Disease protein prediction; Metagraph; Protein representations; Protein-protein interaction; Uniprot keywords; Databases and Information Systems; Medicine and Health Sciences
• 在線閱讀: https://ink.library.smu.edu.sg/sis_research/4230; https://ink.library.smu.edu.sg/context/sis_research/article/5233/viewcontent/Disease_gene_manuscript.pdf
• 機構: Singapore Management University

This chapter is based on exploiting the network-based representations of proteins, metagraphs, in protein-protein interaction network to identify candidate disease-causing proteins. Protein-protein interaction (PPI) networks are effective tools in studying the functional roles of proteins in the development of various diseases. However, they are insufficient without the support of additional biological knowledge for proteins such as their molecular functions and biological processes. To enhance PPI networks, we utilize biological properties of individual proteins as well. More specifically, we integrate keywords from UniProt database describing protein properties into the PPI network and construct a novel heterogeneous PPI-Keyword (PPIK) network consisting of both proteins and keywords. As proteins with similar functional duties or involving in the same metabolic pathway tend to have similar topological characteristics, we propose to represent them with metagraphs. Compared to the traditional network motif or subgraph, a metagraph can capture the topological arrangements through not only the protein-protein interactions but also protein-keyword associations. We feed those novel metagraph representations into classifiers for disease protein prediction and conduct our experiments on three different PPI databases. They show that the proposed method consistently increases disease protein prediction performance across various classifiers, by 15.3% in AUC on average. It outperforms the diffusion-based (e.g., RWR) and the module-based baselines by 13.8–32.9% in overall disease protein prediction. Breast cancer protein prediction outperforms RWR, PRINCE, and the module-based baselines by 6.6–14.2%. Finally, our predictions also exhibit better correlations with literature findings from PubMed database.