ArticleBMC bioinformatics2018
Identifying diseases-related metabolites using random walk.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Dual balanced augmented topological noncoding RNA disease triplet association in heterogeneous graphs.Briefings in bioinformatics · 2025Article
- Identification of metabolite-disease associations based on knowledge graph.Metabolomics : Official journal of the Metabolomic Society · 2025Article
- Deciphering metabolic disease mechanisms for natural medicine discovery via graph autoencoders.Frontiers in pharmacology · 2025Article
- Article
- Metabolic Connectome and Its Role in the Prediction, Diagnosis, and Treatment of Complex Diseases.Metabolites · 2024Review
- Identification of plant vacuole proteins by using graph neural network and contact maps.BMC bioinformatics · 2023Article
- Metabolite-disease interaction prediction based on logistic matrix factorization and local neighborhood constraints.Frontiers in psychiatry · 2023Article
- Golgi_DF: Golgi proteins classification with deep forest.Frontiers in neuroscience · 2023Article
- Predicting Metabolite-Disease Associations Based on LightGBM Model.Frontiers in genetics · 2021Article
- Prediction of Ovarian Cancer-Related Metabolites Based on Graph Neural Network.Frontiers in cell and developmental biology · 2021Article
- Identifying diseases that cause psychological trauma and social avoidance by GCN-Xgboost.BMC bioinformatics · 2020Article
- 6mA-RicePred: A Method for Identifying DNAFrontiers in plant science · 2020Article
- PSBP-SVM: A Machine Learning-Based Computational Identifier for Predicting Polystyrene Binding Peptides.Frontiers in bioengineering and biotechnology · 2020Article
- Its2vec: Fungal Species Identification Using Sequence Embedding and Random Forest Classification.BioMed research international · 2020Article
- Predicting Metabolite-Disease Associations Based on Spy Strategy and ABC Algorithm.Frontiers in molecular biosciences · 2020Article
- MiRNA-disease interaction prediction based on kernel neighborhood similarity and multi-network bidirectional propagation.BMC medical genomics · 2019Article
- Selecting Essential MicroRNAs Using a Novel Voting Method.Molecular therapy. Nucleic acids · 2019Article
- ProbPFP: a multiple sequence alignment algorithm combining hidden Markov model optimized by particle swarm optimization with partition function.BMC bioinformatics · 2019Article
- Predicting Ion Channels Genes and Their Types With Machine Learning Techniques.Frontiers in genetics · 2019Article
- Identification of Phage Viral Proteins With Hybrid Sequence Features.Frontiers in microbiology · 2019Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMetabolites disrupted by abnormal state of human body are deemed as the effect of diseases. In comparison with the cause of diseases like genes, these markers are easier to be captured for the prevention and diagnosis of metabolic diseases. Currently, a large number of metabolic markers of diseases need to be explored, which drive us to do this work.
methodsThe existing metabolite-disease associations were extracted from Human Metabolome Database (HMDB) using a text mining tool NCBO annotator as priori knowledge. Next we calculated the similarity of a pair-wise metabolites based on the similarity of disease sets of them. Then, all the similarities of metabolite pairs were utilized for constructing a weighted metabolite association network (WMAN). Subsequently, the network was utilized for predicting novel metabolic markers of diseases using random walk.
resultsTotally, 604 metabolites and 228 diseases were extracted from HMDB. From 604 metabolites, 453 metabolites are selected to construct the WMAN, where each metabolite is deemed as a node, and the similarity of two metabolites as the weight of the edge linking them. The performance of the network is validated using the leave one out method. As a result, the high area under the receiver operating characteristic curve (AUC) (0.7048) is achieved. The further case studies for identifying novel metabolites of diabetes mellitus were validated in the recent studies.
conclusionIn this paper, we presented a novel method for prioritizing metabolite-disease pairs. The superior performance validates its reliability for exploring novel metabolic markers of diseases.
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