ArticlePloS one2020
A thorough analysis of the contribution of experimental, derived and sequence-based predicted protein-protein interactions for functional annotation of proteins.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- SAFPred: synteny-aware gene function prediction for bacteria using protein embeddings.Bioinformatics (Oxford, England) · 2024Article
- SAP: Synteny-aware gene function prediction for bacteria using protein embeddings.bioRxiv : the preprint server for biology · 2023Article
- In silico and gene expression analysis of the acute inflammatory response of gilthead seabream (Sparus aurata) after subcutaneous administration of carrageenin.Fish physiology and biochemistry · 2021Article
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Authors and funding
3 authors.
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Abstract
Physical interaction between two proteins is strong evidence that the proteins are involved in the same biological process, making Protein-Protein Interaction (PPI) networks a valuable data resource for predicting the cellular functions of proteins. However, PPI networks are largely incomplete for non-model species. Here, we tested to what extent these incomplete networks are still useful for genome-wide function prediction. We used two network-based classifiers to predict Biological Process Gene Ontology terms from protein interaction data in four species: Saccharomyces cerevisiae, Escherichia coli, Arabidopsis thaliana and Solanum lycopersicum (tomato). The classifiers had reasonable performance in the well-studied yeast, but performed poorly in the other species. We showed that this poor performance can be considerably improved by adding edges predicted from various data sources, such as text mining, and that associations from the STRING database are more useful than interactions predicted by a neural network from sequence-based features.
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