BMC medical genomics · 2019
A network embedding model for pathogenic genes prediction by multi-path random walking on heterogeneous network.
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Xu Bo, Liu Yu, Yu Shuo, Wang Lei, Dong Jie, Lin Hongfei, Yang Zhihao, Wang Jian, Xia Feng
Аннотация
BACKGROUND: Prediction of pathogenic genes is crucial for disease prevention, diagnosis, and treatment. But traditional genetic localization methods are often technique-difficulty and time-consuming. With the development of computer science, computational biology has gradually become one of the main methods for finding candidate pathogenic genes.
METHODS: We propose a pathogenic genes prediction method based on network embedding which is called Multipath2vec. Firstly, we construct an heterogeneous network which is called GP-network. It is constructed based on three kinds of relationships between genes and phenotypes, including correlations between phenotypes, interactions between genes and known gene-phenotype pairs. Then in order to embedding the network better, we design the multi-path to guide random walk in GP-network. The multi-path includes multiple paths between genes and phenotypes which can capture complex structural information of heterogeneous network. Finally, we use the learned vector representation of each phenotype and protein to calculate the similarities and rank according to the similarities between candidate genes and the target phenotype.
RESULTS: We implemented Multipath2vec and four baseline approaches (i.e., CATAPULT, PRINCE, Deepwalk and Metapath2vec) on many-genes gene-phenotype data, single-gene gene-phenotype data and whole gene-phenotype data. Experimental results show that Multipath2vec outperformed the state-of-the-art baselines in pathogenic genes prediction task.
CONCLUSIONS: We propose Multipath2vec that can be utilized to predict pathogenic genes and experimental results show the higher accuracy of pathogenic genes prediction.
METHODS: We propose a pathogenic genes prediction method based on network embedding which is called Multipath2vec. Firstly, we construct an heterogeneous network which is called GP-network. It is constructed based on three kinds of relationships between genes and phenotypes, including correlations between phenotypes, interactions between genes and known gene-phenotype pairs. Then in order to embedding the network better, we design the multi-path to guide random walk in GP-network. The multi-path includes multiple paths between genes and phenotypes which can capture complex structural information of heterogeneous network. Finally, we use the learned vector representation of each phenotype and protein to calculate the similarities and rank according to the similarities between candidate genes and the target phenotype.
RESULTS: We implemented Multipath2vec and four baseline approaches (i.e., CATAPULT, PRINCE, Deepwalk and Metapath2vec) on many-genes gene-phenotype data, single-gene gene-phenotype data and whole gene-phenotype data. Experimental results show that Multipath2vec outperformed the state-of-the-art baselines in pathogenic genes prediction task.
CONCLUSIONS: We propose Multipath2vec that can be utilized to predict pathogenic genes and experimental results show the higher accuracy of pathogenic genes prediction.