title: Network-based characterization and prediction of human DNA repair genes and pathways.
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.
authors.affiliation: Institute of Cardiovascular Sciences and Key Laboratory of Molecular Cardiovascular Sciences, Ministry of Education, Peking University Health Science Center, Beijing, P. R. China.
authors.forename: Yan-Hui
authors.initials: YH
authors.lastname: Li
authors.affiliation: Special Medical Ward (Geratology Department) First Hospital of Tsinghua University Beijing, P. R. China.
authors.forename: Gai-Gai
authors.initials: GG
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D004260:DNA Repair; D045643:DNA Repair Enzymes / Q000235:genetics*; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D053263:Gene Regulatory Networks; D006801:Humans; D000069550:Machine Learning; D060066:Protein Interaction Maps; D060388:Support Vector Machine
chemical_list: D045643:DNA Repair Enzymes