Drug Combination Side Effect Prediction Based on Polypharmacy Network and GraphSAGE Algorithm
Bibliographic record
Abstract
Due to the complexity and diversity of modern diseases, the combination of drugs has become the first choice. According to the graph theory in graph theory, we transform the original link prediction problem into the node identification problem by establishing the graph of drug side effect network - polypharmacy network to predict. A combined drug side effect prediction model (CDSG) was constructed on the polypharmacy network graph, based on graph attention mechanism and Graph Sample and Aggregate (GraphSAGE). Firstly, the side-effect network was constructed by using the side-effect relationship between drugs and drugs, and then the polypharmacy network was constructed. Then the target gene of the drug is encoded and the feature vector of the drug is established. Furthermore, the advanced feature representations of drugs are learned by utilizing the graph attention network and the GraphSAGE algorithm. Finally, the advanced drug characteristics were connected to the fully connected layer for classification prediction. On a baseline dataset of 1138 side effect types of 257 drugs, we conducted different methods of feature fusion experiment, ablation experiment, 5-fold crossover experiment, and compared CDSG with several computing models such as traditional GCN model, the GAT model and matrix decomposition models, and the final model achieved good results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".