Deep Learning-Based Genetic Perturbation Models <i>Do</i> Outperform Uninformative Baselines on Well-Calibrated Metrics
Bibliographic record
Abstract
Single cell genetic perturbation modeling involves predicting the effects of unobserved genetic manipulations, enabling scalable in silico screens for target discovery. Recent reports have claimed that deep learning-based perturbation models fail to outperform uninformative baselines, raising doubts about their utility. Here, we show that these conclusions largely stem from limitations of benchmarking metrics , not from the models themselves. We introduce a framework for evaluating bench-mark metric calibration using positive and negative controls, including a new positive control baseline (the interpolated duplicate ) and a quantitative calibration measure (the dynamic range fraction ). Across 14 perturbation datasets and 13 evaluation metrics, we find that conventional metrics such as mean squared error (MSE) and control-referenced delta correlation (Pearson(Δ ctrl )) are often poorly calibrated, whereas weighted and rank-based alternatives exhibit consistent calibration. Under well-calibrated metrics, deep learning models outperform mean, control, and linear baselines, and in some cases even surpass the additive baseline in combination-prediction tasks. Calibrated evaluation thus explains prior reports of model underperformance, revealing that deep learning models do outperform uninformative baselines.
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 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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".