ArticleScientific reports2017
Network-based characterization and prediction of human DNA repair genes and pathways.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
The trial behind it
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Who cites it
2 citing papers in PubMed, 2 citations in OpenAlex.
- Accurate prediction of candidate lncRNAs associated with DNA damage response based on gene expression patterns from graph neural networks.Bioinformatics advances · 2026Article
- DNA repair pathways and their roles in drug resistance for lung adenocarcinoma.Molecular biology reports · 2021Review
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Network biology is a useful strategy to understand cell's functional organization. In this study, for the first time, we successfully introduced network approaches to study properties of human DNA repair genes. Compared with non-DNA repair genes, we found distinguishing features for DNA repair genes: (i) they tend to have higher degrees; (ii) they tend to be located at global network center; (iii) they tend to interact directly with each other. Based on these features, we developed the first algorithm to predict new DNA repair genes. We tested several machine-learning models and found that support vector machine with kernel function of radial basis function (RBF) achieve the best performance, with precision = 0.74 and area under curve (AUC) = 0.96. In the end, we applied the algorithm to predict new DNA repair genes and got 32 new candidates. Literature supporting four of the predictions was found. We believe the network approaches introduced here might open a new avenue to understand DNA repair genes and pathways. The suggested algorithm and the predicted genes might be helpful for scientists in the field.
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Registered trials
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