Inference of Gene Regulatory Networks Based on Heterogeneous Graph Neural Networks
Bibliographic record
Abstract
Gene Regulatory Networks (GRNs) are central to understanding the mechanisms of gene expression regulation, yet their construction is challenged by node heterogeneity and complex regulatory relationships. Traditional methods often simplify GRNs into homogeneous graphs, overlooking the functional differences between genes and regulatory factors. To address this limitation, we propose a novel GRN construction method, HGRN, based on Heterogeneous Graph Convolutional Networks. By modeling GRNs as heterogeneous graphs comprising two types of nodes—genes and regulatory factors—along with multiple regulatory relationships, and incorporating a multi-channel graph convolution mechanism, our model can separately learn gene expression features and regulatory factor functional features while capturing high-order regulatory dependencies. Experiments on non-specific ChIP-seq datasets demonstrate that this approach outperforms traditional methods in predicting regulatory relationships, significantly improving the accuracy of GRN construction. This study provides a new perspective for the precise inference of gene regulatory networks and offers a powerful tool for elucidating disease mechanisms and predicting drug targets in biomedical research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".