Name
Title | ||
|---|---|---|
Semi-supervised prediction of human miRNA-disease association based on graph regularization framework in heterogeneous networks. |
Abstract | ||
|---|---|---|
•We use graph regularization framework to address difficulties in obtaining negative miRNA-disease association samples.•More biological information are integrated to improve the reliability of miRNA similarity.•MDAGRF could obtain a single classifier to integrate homogeneous space and heterogeneous space.•MDAGRF is a global approach which can reconstruct the missing associations for all diseases simultaneously. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.neucom.2018.03.003 | Neurocomputing |
Keywords | Field | DocType |
Semi-supervised prediction,Heterogeneous network,miRNA-disease associations,Graph regularization framework,Transductive learning | Disease,Disease Association,Graph regularization,Artificial intelligence,Heterogeneous network,Cross-validation,Mathematics,Machine learning | Journal |
Volume | ISSN | Citations |
294 | 0925-2312 | 0 |
PageRank | References | Authors |
0.34 | 22 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiawei Luo | 1 | 128 | 23.65 |
| Pingjian Ding | 2 | 20 | 4.13 |
| Cheng Liang | 3 | 60 | 9.31 |
| Xiangtao Chen | 4 | 20 | 4.07 |