Drug Repositioning via Multi-View Representation Learning With Heterogeneous Graph Neural Network
Bibliographic record
Abstract
Exploring simple and efficient computational methods for drug repositioning has emerged as a popular and compelling topic in the realm of comprehensive drug development. The crux of this technology lies in identifying potential drug-disease associations, which can effectively mitigate the burdens caused by the exorbitant costs and lengthy periods of conventional drugs development. However, existing computational drug repositioning methods continue to encounter challenges in accurately predicting associations between drugs and diseases. In this paper, we propose a Multi-view Representation Learning method (MRLHGNN) with Heterogeneous Graph Neural Network for drug repositioning. This method is based on a collection of data from multiple biological entities associated with drugs or diseases. It consists of a view-specific feature aggregation module with meta-paths and auto multi-view fusion encoder. To better utilize local structural and semantic information from specific views in heterogeneous graph, MRLHGNN employs a feature aggregation model with variable-length meta-paths to expand the local receptive field. Additionally, it utilizes a transformer based semantic aggregation module to aggregate semantic features across different view-specific graphs. Finally, potential drug-disease associations are obtained through a multi-view fusion decoder with an attention mechanism. Cross-validation experiments demonstrate the effectiveness and interpretability of the MRLHGNN in comparison to nine state-of-the-art approaches. Case studies further reveal that MRLHGNN can serve as a powerful tool for drug repositioning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".