Name
Title | ||
|---|---|---|
Relation path feature embedding based convolutional neural network method for drug discovery. |
Abstract | ||
|---|---|---|
In this paper, we propose a relation path features embedding based convolutional neural network with attention mechanism for discovering potential drugs from literature. Our method could be an auxiliary method for drug discovery, which can speed up the discovery of new drugs for the incurable diseases. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1186/s12911-019-0764-5 | BMC Med. Inf. & Decision Making |
Keywords | Field | DocType |
Convolutional neural network,Drug discovery,Knowledge graph,Literature-based discovery,Path ranking algorithm | Data mining,Drug discovery,Knowledge graph,Embedding,Convolutional neural network,Drug development,Artificial intelligence,Literature-based discovery,Health informatics,Medicine,Machine learning | Journal |
Volume | Issue | ISSN |
19-S | 2 | 1472-6947 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Di Zhao | 1 | 1 | 3.07 |
| Jian Wang | 2 | 112 | 18.98 |
| Shengtian Sang | 3 | 14 | 2.95 |
| Hongfei Lin | 4 | 768 | 122.52 |
| Jiabin Wen | 5 | 0 | 0.34 |
| Chun-Mei Yang | 6 | 4 | 1.28 |