Name
Abstract | ||
|---|---|---|
BackgroundIn the context of drug discovery, drug target interactions (DTIs) can be predicted based on observed topological features of a semantic network across the chemical and biological space. In a semantic network, the types of the nodes and links are different. In order to take into account the heterogeneity of the semantic network, meta-path-based topological patterns were investigated for link prediction. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1186/s12859-016-1005-x | BMC Bioinformatics |
Keywords | Field | DocType |
Link prediction,Machine learning,Meta-path topological feature,Random forest,Semantic network analysis | Data mining,Drug discovery,Computer science,Semantic network analysis,Semantic network,Drug target,Artificial intelligence,Bioinformatics,Random forest,Machine learning,Semantic computing | Journal |
Volume | Issue | ISSN |
17 | 1 | 1471-2105 |
Citations | PageRank | References |
13 | 0.52 | 19 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gang Fu | 1 | 207 | 9.67 |
| Ying Ding | 2 | 2396 | 144.65 |
| Abhik Seal | 3 | 38 | 2.84 |
| Bin Chen | 4 | 240 | 12.61 |
| Yizhou Sun | 5 | 3446 | 143.93 |
| Evan E Bolton | 6 | 827 | 69.52 |