ArticleBioinformatics advances2022
Biclique extension as an effective approach to identify missing links in metabolic compound-protein interaction networks.
Article in Bioinformatics advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 9 citations in OpenAlex.
- PMPIHGLL: predicting metabolite-protein interactions using dual hypergraph convolutional networks and large language models.Briefings in bioinformatics · 2026Article
- Mapping protein-metabolite interactions iniScience · 2025Article
- Network-based drug repurposing for HPV-associated cervical cancer.Computational and structural biotechnology journal · 2023Article
- Network Pharmacology Analysis and Experimental Validation to Investigate the Mechanism of Total Flavonoids of Rhizoma Drynariae in Treating Rheumatoid Arthritis.Drug design, development and therapy · 2022Article
Corrections and comments
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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
Motivation: Metabolic networks are complex systems of chemical reactions proceeding via physical interactions between metabolites and proteins. We aimed to predict previously unknown compound-protein interactions (CPI) in metabolic networks by applying biclique extension, a network-structure-based prediction method. Results: We developed a workflow, named BiPredict, to predict CPIs based on biclique extension and applied it to Availability and implementation: BiPredict and related datasets are available at https://github.com/SandraThieme/BiPredict. Supplementary information: Supplementary data are available at
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Registered trials
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