Name
Abstract | ||
|---|---|---|
A learning-based SVM scoring model with structural features for specific protein-DNA binding and an atomic-level protein-DNA interaction potential DDNA3 significantly improves prediction accuracy of complex models by successfully identifying cases without near-native structural models. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1186/s12859-018-2538-y | BMC Bioinformatics |
Keywords | Field | DocType |
Knowledge-based potential,Protein-DNA binding,Rigid docking,Support vector machine,Transcription factor | Biology,Docking (dog),Support vector machine,DNA-binding protein,DNA,Computational biology,Binding selectivity,Genetics,Transcription factor,DNA microarray | Journal |
Volume | Issue | ISSN |
19 | Suppl 20 | 1471-2105 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rosario I Corona | 1 | 5 | 1.87 |
| Sanjana Sudarshan | 2 | 0 | 0.34 |
| Aluru, Srinivas | 3 | 1166 | 122.83 |
| Juntao Guo | 4 | 15 | 5.49 |