DEPRECATED_DREAM-RNN Human Model Predictor (Rafi et al. 2024) using the Genomic API for Model Evaluation (GAME) Framework
Bibliographic record
Abstract
This record provides a Predictor container for the DREAM-RNN model (Rafi et al. 2024, Nature Biotechnology). DREAM-RNN is a deep learning model optimized through the Prix Fixe framework, which was developed to systematically evaluate model architectures and training strategies from the Random Promoter DREAM Challenge. The Predictor container (dream_rnn_predictor.sif) includes: - Predictor script for sequence processing and error handling. - Integrated DREAM-RNN model with its dependencies and DREAM conda environment created using dreamRNN_environment.yml. - Pre-trained model weights (model_best.pth) for predictions. - Support scripts like: api_preprocessing_utils.py error_message_functions_updated.py predictor_help_message.json. - Dependencies required by the Predictor. Running the container: Ensure Apptainer is intalled in the system the container is intended to run. Always run the Predictor first, so it can listen for incoming connections from Evaluators: apptainer run --containall dream_rnn_predictor.sif HOST_IP HOST_PORT Additional information can be found on GitHub: Genomic API for Model Evaluation DREAM-RNN Predictor-specific information can be found within the same repository: DREAM-RNN Code repository for the DREAM Challenge models: DREAM Challenge 2022 Github The paper can be found here: Rafi, A.M., Nogina, D., Penzar, D. et al. A community effort to optimize sequence-based deep learning models of gene regulation. Nat Biotechnol 43, 1373–1383 (2025). https://doi.org/10.1038/s41587-024-02414-w
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.107 | 0.081 |
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".