HerGePred: Heterogeneous Network Embedding Representation for Disease Gene Prediction
The discovery of disease causing genes is a critical step towards understanding the nature of a disease and determining a possible cure for it. In recent years, many computational methods to identify disease genes have been proposed. However, making full use of disease related (e.g. symptoms) and gene related (e.g. gene ontology and protein protein interactions) information to improve the performance of disease gene prediction is still an issue. Here, we develop a heterogeneous disease gene related network (HDGN) embedding representation framework for disease gene prediction (called HerGePred) Based on this framework, a low dimensional vector representation (LVR) of the nodes in the HDGN can be obtained. Then, we propose two specific algorithms, namely, an LVR based similarity prediction and a random walk with restart on a reconstructed heterogeneous disease gene network (RW RDGN) to predict disease genes with high performance. First, to validate the rationality of the framework, we analyze the similarity based overlap distribution of disease pairs and design an experiment for disease gene association recovery, the results of which revealed that the LVR of nodes performs well at preserving the local and global network structure of the HDGN. Then, we apply tenfold cross validation and external validation to compare our methods with other well known disease gene prediction algorithms. The experimental results show that the RW RDGN performs better than the state of the art algorithm. The prediction results of disease candidate genes are essential for molecular mechanism investigation and experimental validation. The source codes of HerGePred and experimental data are available at https:/github.com/yangkuoone/HerGePred.
IEEE Journal of Biomedical and Health Informatics