Name
Title | ||
|---|---|---|
Integrative graph regularized matrix factorization for drug-pathway associations analysis. |
Abstract | ||
|---|---|---|
We propose a novel drug-pathway association identification method: Integrative Graph regularized Matrix Factorization (IGMF). It employs graph regularization to encode data geometrical information and prevent possible overfitting in prediction. Furthermore, it achieves parts-based and sparse data representation by imposing L1-norm regularization on the objective function. Empirical studies demonstrate that IGMF has strong advantages in identifying more new drug-pathway associations compared to its peer methods. It further shows a good capability to unveil the intrinsic structures of data. As an effective drug-pathway discovery method, it will inspire new analytics methods in this subfield. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.compbiolchem.2018.11.026 | Computational Biology and Chemistry |
Keywords | Field | DocType |
Pathway-based,Drug-pathway associations,Graph regularized,Integrative matrix factorization | ENCODE,Drug discovery,Biology,DECIPHER,Matrix decomposition,Regularization (mathematics),Artificial intelligence,Overfitting,Genetics,Analytics,Sparse matrix,Machine learning | Journal |
Volume | ISSN | Citations |
78 | 1476-9271 | 0 |
PageRank | References | Authors |
0.34 | 8 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ling-yun Dai | 1 | 5 | 5.85 |
| Chun-hou Zheng | 2 | 732 | 71.79 |
| Liu Jin-Xing | 3 | 40 | 16.11 |
| Rong Zhu | 4 | 62 | 24.70 |
| Shasha Yuan | 5 | 2 | 6.10 |
| Juan Wang | 6 | 0 | 2.70 |
| Xiang-Zhen Kong | 7 | 87 | 6.04 |