Research paper
Other ComputationalHigh Throughput ScreeningGAN-TAT: A Novel Framework Using Protein Interaction Networks in Druggable Gene Identification
George Y. Wang, Srisharan Murugesan, Aditya P. Rohatgi
Each sample groups how it was prepared, characterized, and measured. Click a card to expand.
4 properties
3 properties
3 properties
| Type | Label | Value | Material | Sample | Condition | Source |
|---|---|---|---|---|---|---|
| Auc Roc | XGBoost + ImGAGN-GraphSAGE (GAN-TAT), Tclin | 0.951 | — | |||
| Auc Roc | XGBoost + ImGAGN-GraphSAGE (GAN-TAT), pancreatic | 0.919 | — | |||
| Auc Roc | XGBoost + ImGAGN-GraphSAGE (GAN-TAT), leukemia | 0.931 | — | |||
| Auc Roc | XGBoost + LINE, Tclin | 0.947 | — | |||
| Auc Roc | XGBoost + LINE, pancreatic | 0.899 | — | |||
| Auc Roc | XGBoost + LINE, leukemia | 0.929 | — | |||
| Auc Roc | XGBoost + Node2Vec, Tclin | 0.94 | — | |||
| Auc Roc | XGBoost + Node2Vec, pancreatic | 0.892 | — | |||
| Auc Roc | XGBoost + Node2Vec, leukemia | 0.914 | — | |||
| Fisher Exact P Value | Tclin enrichment in top 5% predictions | <2.4 × 10−266 | — |
Each sample groups how it was prepared, characterized, and measured. Click a card to expand.
4 properties
3 properties
3 properties
| Type | Label | Value | Material | Sample | Condition | Source |
|---|---|---|---|---|---|---|
| Auc Roc | XGBoost + ImGAGN-GraphSAGE (GAN-TAT), Tclin | 0.951 | — | |||
| Auc Roc | XGBoost + ImGAGN-GraphSAGE (GAN-TAT), pancreatic | 0.919 | — | |||
| Auc Roc | XGBoost + ImGAGN-GraphSAGE (GAN-TAT), leukemia | 0.931 | — | |||
| Auc Roc | XGBoost + LINE, Tclin | 0.947 | — | |||
| Auc Roc | XGBoost + LINE, pancreatic | 0.899 | — | |||
| Auc Roc | XGBoost + LINE, leukemia | 0.929 | — | |||
| Auc Roc | XGBoost + Node2Vec, Tclin | 0.94 | — | |||
| Auc Roc | XGBoost + Node2Vec, pancreatic | 0.892 | — | |||
| Auc Roc | XGBoost + Node2Vec, leukemia | 0.914 | — | |||
| Fisher Exact P Value | Tclin enrichment in top 5% predictions | <2.4 × 10−266 | — |
Each sample groups how it was prepared, characterized, and measured. Click a card to expand.
4 properties
3 properties
3 properties
| Type | Label | Value | Material | Sample | Condition | Source |
|---|---|---|---|---|---|---|
| Auc Roc | XGBoost + ImGAGN-GraphSAGE (GAN-TAT), Tclin | 0.951 | — | |||
| Auc Roc | XGBoost + ImGAGN-GraphSAGE (GAN-TAT), pancreatic | 0.919 | — | |||
| Auc Roc | XGBoost + ImGAGN-GraphSAGE (GAN-TAT), leukemia | 0.931 | — | |||
| Auc Roc | XGBoost + LINE, Tclin | 0.947 | — | |||
| Auc Roc | XGBoost + LINE, pancreatic | 0.899 | — | |||
| Auc Roc | XGBoost + LINE, leukemia | 0.929 | — | |||
| Auc Roc | XGBoost + Node2Vec, Tclin | 0.94 | — | |||
| Auc Roc | XGBoost + Node2Vec, pancreatic | 0.892 | — | |||
| Auc Roc | XGBoost + Node2Vec, leukemia | 0.914 | — | |||
| Fisher Exact P Value | Tclin enrichment in top 5% predictions | <2.4 × 10−266 | — |
Each sample groups how it was prepared, characterized, and measured. Click a card to expand.
4 properties
3 properties
3 properties
| Type | Label | Value | Material | Sample | Condition | Source |
|---|---|---|---|---|---|---|
| Auc Roc | XGBoost + ImGAGN-GraphSAGE (GAN-TAT), Tclin | 0.951 | — | |||
| Auc Roc | XGBoost + ImGAGN-GraphSAGE (GAN-TAT), pancreatic | 0.919 | — | |||
| Auc Roc | XGBoost + ImGAGN-GraphSAGE (GAN-TAT), leukemia | 0.931 | — | |||
| Auc Roc | XGBoost + LINE, Tclin | 0.947 | — | |||
| Auc Roc | XGBoost + LINE, pancreatic | 0.899 | — | |||
| Auc Roc | XGBoost + LINE, leukemia | 0.929 | — | |||
| Auc Roc | XGBoost + Node2Vec, Tclin | 0.94 | — | |||
| Auc Roc | XGBoost + Node2Vec, pancreatic | 0.892 | — | |||
| Auc Roc | XGBoost + Node2Vec, leukemia | 0.914 | — | |||
| Fisher Exact P Value | Tclin enrichment in top 5% predictions | <2.4 × 10−266 | — |