ArticleBioinformatics advances2023
Prediction of metabolite-protein interactions based on integration of machine learning and constraint-based modeling.
Article in Bioinformatics advances, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed.
- PMPIHGLL: predicting metabolite-protein interactions using dual hypergraph convolutional networks and large language models.Briefings in bioinformatics · 2026Article
- Metabolite-mediated plant immunity: From traditional defenders to artificial elicitors.Journal of integrative plant biology · 2026Review
- Accurate prediction of flux distributions compatible with metabolite concentration effects in genome-scale metabolic networks.PLoS computational biology · 2026Article
- Machine learning of metabolite-protein interactions from model-derived metabolic phenotypes.NAR genomics and bioinformatics · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: Metabolite-protein interactions play an important role in regulating protein functions and metabolism. Yet, predictions of metabolite-protein interactions using genome-scale metabolic networks are lacking. Here, we fill this gap by presenting a computational framework, termed SARTRE, that employs features corresponding to shadow prices determined in the context of flux variability analysis to predict metabolite-protein interactions using supervised machine learning. Results: By using gold standards for metabolite-protein interactomes and well-curated genome-scale metabolic models of Availability and implementation: https://github.com/fayazsoleymani/SARTRE.
Identifiers
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Registered trials
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