期刊
PLOS ONE
卷 14, 期 11, 页码 -出版社
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0225380
关键词
-
资金
- National Natural Science Foundation of China [61972451, 61672334, 61902230]
- Fundamental Research Funds for the Central Universities [GK201901010]
Metabolites play a significant role in various complex human disease. The exploration of the relationship between metabolites and diseases can help us to better understand the underlying pathogenesis. Several network-based methods have been used to predict the association between metabolite and disease. However, some methods ignored hierarchical differences in disease network and failed to work in the absence of known metabolite-disease associations. This paper presents a bi-random walks based method for disease-related metabolites prediction, called MDBIRW. First of all, we reconstruct the disease similarity network and metabolite functional similarity network by integrating Gaussian Interaction Profile (GIP) kernel similarity of diseases and GIP kernel similarity of metabolites, respectively. Then, the bi-random walks algorithm is executed on the reconstructed disease similarity network and metabolite functional similarity network to predict potential disease-metabolite associations. At last, MDBIRW achieves reliable performance in leave-one-out cross validation (AUC of 0.910) and 5-fold cross validation (AUC of 0.924). The experimental results show that our method outperforms other existing methods for predicting disease-related metabolites.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据