ArticleComputational and structural biotechnology journal2021
Combination of network and molecule structure accurately predicts competitive inhibitory interactions.
Article in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Machine learning of metabolite-protein interactions from model-derived metabolic phenotypes.NAR genomics and bioinformatics · 2024Article
- Prediction of metabolite-protein interactions based on integration of machine learning and constraint-based modeling.Bioinformatics advances · 2023Article
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Authors and funding
5 authors.
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Abstract
Mining of metabolite-protein interaction networks facilitates the identification of design principles underlying the regulation of different cellular processes. However, identification and characterization of the regulatory role that metabolites play in interactions with proteins on a genome-scale level remains a pressing task. Based on availability of high-quality metabolite-protein interaction networks and genome-scale metabolic networks, here we propose a supervised machine learning approach, called CIRI that determines whether or not a metabolite is involved in a
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