Gene Regulatory Network Inference Based on Convolutional GRU
Why this work is in the frame
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Bibliographic record
Abstract
Time-course single-cell RNA sequencing (scRNA-seq) data reflect gene expression changes over time, offering a valuable resource for exploring dynamic gene interactions and building dynamic gene regulatory networks (GRNs). However, most existing methods are typically designed for bulk RNA sequencing (bulk RNA-seq) data and cannot be directly applied to time-course scRNA-seq data. Addressing this issue, we present CGGRN, an approach based on convolutional gated recurrent unit (GRU) for inferring GRNs. CGGRN transforms time-course data into images, including raw pairwise gene images and neighborhood images, and aggregates them with time point information into a four-dimensional tensor. The tensor is then fed into the convolutional GRU to capture features for each gene pair and reconstruct the GRN. We conducted trials on four time-course scRNA-seq datasets using CGGRN, and the outcomes show that CGGRN surpasses existing models in constructing GRN.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it