ArticlePloS one2019
Prediction of disease-related metabolites using bi-random walks.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 28 citations in OpenAlex.
- A dual-view fusion framework using residual gated graph convolution and Mamba for metabolite-disease association prediction with explorations on salivary metabolites.Journal of computer-aided molecular design · 2026Article
- Adaptive Graph Prompting Meets Contrastive Learning: A Multi-View Framework for Metabolite-Disease Association Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Metabolomic insights of cultured meat compared to conventional meat.Scientific reports · 2025Article
- Predicting Disease-Metabolite Associations Based on the Metapath Aggregation of Tripartite Heterogeneous Networks.Interdisciplinary sciences, computational life sciences · 2024Article
- Metabolic Connectome and Its Role in the Prediction, Diagnosis, and Treatment of Complex Diseases.Metabolites · 2024Review
- Common alterations in plasma free amino acid profiles and gut microbiota-derived tryptophan metabolites of five types of cancer patients.Amino acids · 2023Article
- Article
- Metabolite-disease interaction prediction based on logistic matrix factorization and local neighborhood constraints.Frontiers in psychiatry · 2023Article
- Inferring Retinal Degeneration-Related Genes Based on Xgboost.Frontiers in molecular biosciences · 2022Article
- Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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