Disease gene prediction based on heterogeneous probabilistic hypergraph ranking

Ding, Feng; Kong, Xiangjie; Zhao, Zhehuan; Xia, Feng; Liu, Anfu; Bai, Chenxu; Xu, Bo; Liu, Xiaoxia; Sang, Shengtian; Lin, Hongfei; Yang, Zhihao; Wang, Jian

Title: Disease gene prediction based on heterogeneous probabilistic hypergraph ranking
Creator: Ding, Feng; Kong, Xiangjie; Zhao, Zhehuan; Xia, Feng; Liu, Anfu; Bai, Chenxu; Xu, Bo; Liu, Xiaoxia; Sang, Shengtian; Lin, Hongfei; Yang, Zhihao; Wang, Jian
Date: 2019
Type: Text; Conference proceedings
Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/183882
Identifier: vital:16369
Identifier: https://doi.org/10.1109/BIBM47256.2019.8982999
Identifier: ISBN:978-1-7281-1867-3
Abstract: In order to save time and cost, many disease gene prediction methods have been proposed in recent years. However, the traditional network model uses a binary relationship to represent the relationship between different proteins or gene molecules and phenotypes, which leads to the loss of information. Recently, hypergraph shows that it can overcome this loss of information to some extent and preserve the multivariate relationship, so we transformed the disease gene prediction problem into the problem of ranking the multivariate-relationship object. In this paper, we propose a method of Heterogeneous Probabilistic Hypergraph Ranking (HPHR) to predict disease genes. Firstly, fix a graph centroid for each hyperedge and according to different associations, and add other nodes related to the graph centroid to hyperedges with a certain probability. Then transform the problem of predicting disease genes into the problem of ranking heterogeneous objects, and the candidate genes are sorted by hypergraph ranking. The method is then applied to the integrated disease gene network. Compared with other prediction methods achieved better results, which was verified by this experiment.
Publisher: IEEE
Relation: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM); San Diego, CA, USA; 18-21 November 2019 p. 2021-2028
Rights: All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
Rights: Copyright IEEE
Subject: Disease gene prediction; Heterogeneous network; Hypergraph ranking
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