Title | ||
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Characterization and identification of lysine glutarylation based on intrinsic interdependence between positions in the substrate sites. |
Abstract | ||
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The SVM model integrating MDD-identified substrate motifs performed well, with a sensitivity of 0.677, a specificity of 0.619, an accuracy of 0.638, and a Matthews Correlation Coefficient (MCC) value of 0.28. Using an independent testing dataset (46 glutarylated and 92 non-glutarylated sites) obtained from the literature, we demonstrated that the integrated SVM model could improve the predictive performance effectively, yielding a balanced sensitivity and specificity of 0.652 and 0.739, respectively. This integrated SVM model has been implemented as a web-based system (MDDGlutar), which is now freely available at http://csb.cse.yzu.edu.tw/MDDGlutar/ . |
Year | DOI | Venue |
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2019 | 10.1186/s12859-018-2394-9 | BMC Bioinformatics |
Keywords | Field | DocType |
Intrinsic interdependence,Maximal dependence decomposition,Protein glutarylation | Substrate (chemistry),Residue (complex analysis),Biology,Amino acid composition,Molecule,Biochemistry,Lysine,Genetics,DNA microarray | Journal |
Volume | Issue | ISSN |
19 | 13 | 1471-2105 |
Citations | PageRank | References |
0 | 0.34 | 19 |
Authors | ||
5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Kai-Yao Huang | 1 | 115 | 7.91 |
Hui-Ju Kao | 2 | 53 | 4.59 |
Justin Bo-Kai Hsu | 3 | 108 | 6.69 |
Shun-Long Weng | 4 | 30 | 3.72 |
Tzong-Yi Lee | 5 | 617 | 37.18 |