Name
Title | ||
|---|---|---|
Predicting associations among drugs, targets and diseases by tensor decomposition for drug repositioning |
Abstract | ||
|---|---|---|
Development of new drugs is a time-consuming and costly process, and the cost is still increasing in recent years. However, the number of drugs approved by FDA every year per dollar spent on development is declining. Drug repositioning, which aims to find new use of existing drugs, attracts attention of pharmaceutical researchers due to its high efficiency. A variety of computational methods for drug repositioning have been proposed based on machine learning approaches, network-based approaches, matrix decomposition approaches, etc. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1186/s12859-019-3283-6 | BMC Bioinformatics |
Keywords | Field | DocType |
Drug repositioning, Drug-target-disease associations, Tensor decomposition, Clustering | Topological data analysis,Drug repositioning,Biology,Repurposing,Computational biology,Bioinformatics,Cluster analysis,Tensor decomposition | Journal |
Volume | Issue | ISSN |
20 | 26 | 1471-2105 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ran Wang | 1 | 15 | 2.04 |
| Shuai Li | 2 | 192 | 23.09 |
| Lixin Cheng | 3 | 24 | 2.80 |
| Man Hon Wong | 4 | 814 | 233.13 |
| Kwong-Sak Leung | 5 | 1887 | 205.58 |