Name
Abstract | ||
|---|---|---|
A key issue in drug development is to understand the hidden relationships among drugs and targets. Computational methods for novel drug target predictions can greatly reduce time and costs compared with experimental methods. In this paper, we propose a network based computational approach for novel drug and target association predictions. More specifically, a heterogeneous drug-target graph, which incorporates known drug-target interactions as well as drug-drug and target-target similarities, is first constructed. Based on this graph, a novel graph-based inference method is introduced. Compared with two state-of-the-art methods, large-scale cross-validation results indicate that the proposed method can greatly improve novel target predictions. |
Year | Venue | Field |
|---|---|---|
2013 | Pacific Symposium on Biocomputing | Data mining,Graph,Drug discovery,Computer science,Drug development,Inference,Drug target,Bioinformatics,Computer graphics |
DocType | ISSN | Citations |
Conference | 2335-6936 | 2 |
PageRank | References | Authors |
0.39 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenhui Wang | 1 | 19 | 1.46 |
| Sen Yang | 2 | 2 | 3.09 |
| Jing Li | 3 | 498 | 50.65 |