ArticleFrontiers in genetics2021
Predicting Metabolite-Disease Associations Based on LightGBM Model.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Adaptive Graph Prompting Meets Contrastive Learning: A Multi-View Framework for Metabolite-Disease Association Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- GMC-DMA: GNN-Mamba Co-Contrastive Optimization for Disease-Metabolite Association Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Machine learning prediction of cardiovascular disease risk progression from sulfur dioxide exposure in longitudinal population studies in China.BMC cardiovascular disorders · 2026Article
- Unraveling the difference in aroma characteristics of tomato flesh with different colors using HS-SPME-GC-MS/MS andFood chemistry: X · 2026Article
- Research on predicting risk factors for re-bleeding in the acute phase of intracerebral hemorrhage using machine learning algorithms.Frontiers in medicine · 2026Article
- Identification of age-specific urinary metabolic biomarkers in Wilson disease using machine learning: a comparative study of ensemble tree models.Open medicine (Warsaw, Poland) · 2026Article
- ACLPred: an explainable machine learning and tree-based ensemble model for anticancer ligand prediction.Scientific reports · 2025Article
- Deciphering metabolic disease mechanisms for natural medicine discovery via graph autoencoders.Frontiers in pharmacology · 2025Article
- Application of LightGBM hybrid model based on TPE algorithm optimization in sleep apnea detection.Frontiers in neuroscience · 2024Article
- Systemic lupus erythematosus with high disease activity identification based on machine learning.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2023Article
- OEDL: an optimized ensemble deep learning method for the prediction of acute ischemic stroke prognoses using union features.Frontiers in neurology · 2023Article
- Ensemble learning based on efficient features combination can predict the outcome of recurrence-free survival in patients with hepatocellular carcinoma within three years after surgery.Frontiers in oncology · 2022Article
- Geometric complement heterogeneous information and random forest for predicting lncRNA-disease associations.Frontiers in genetics · 2022Article
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3 authors.
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Abstract
Metabolites have been shown to be closely related to the occurrence and development of many complex human diseases by a large number of biological experiments; investigating their correlation mechanisms is thus an important topic, which attracts many researchers. In this work, we propose a computational method named LGBMMDA, which is based on the Light Gradient Boosting Machine (LightGBM) to predict potential metabolite-disease associations. This method extracts the features from statistical measures, graph theoretical measures, and matrix factorization results, utilizing the principal component analysis (PCA) process to remove noise or redundancy. We evaluated our method compared with other used methods and demonstrated the better areas under the curve (AUCs) of LGBMMDA. Additionally, three case studies deeply confirmed that LGBMMDA has obvious superiority in predicting metabolite-disease pairs and represents a powerful bioinformatics tool.
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