Gene-level logistic regression (Kendiukhov 2026)
Logistic regression on mean expression, variance and dropout rate of the candidate target gene, 5-fold GroupKFold by perturbation.
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Evaluations and results
2 evaluations · 2 results. Different protocols are not a single leaderboard.
Protocol: Gene-level logistic regression under 5-fold GroupKFold by perturbation (Kendiukhov 2026)
Sorted by AUROC (cross-validated AUROC over perturbation-gene pairs, 5-fold GroupKFold by perturbation) (higher is better). The best value in each column is highlighted. Decimals are rounded for display; each value links to the printed value and its source.
| Tested configuration | Protocol and dataset | AUROC (cross-validated AUROC over perturbation-gene pairs, 5-fold GroupKFold by perturbation) |
|---|---|---|
| Gene-level logistic regression (Kendiukhov 2026)Evaluation | Replogle genome-scale CRISPRi, RPE1, 1,167 perturbations, confound battery (Kendiukhov 2026) | 0.942 (best in column) |
| Gene-level logistic regression (Kendiukhov 2026)Evaluation | Replogle genome-scale CRISPRi, K562, 280 evaluable perturbations (Kendiukhov 2026) | 0.895 |
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Sources and history
Release 2026-11-02-a7f03c9e55a5 · Record review: source checked
1 source record and release history
- Systematic evaluation of single-cell foundation model interpretability: attention-derived edge scores add no incremental value over gene-level features for perturbation-target prediction (Kendiukhov 2026, BMC Genomics 27:634) · Original source · Version of record, BMC Genomics 27:634, published 2026-07-22 (received 2026-03-10, accepted 2026-05-14)
Technical metadata and extraction receipts
Stable ID: gfm-guides-20261014-kendiukhov2026-config-gene-level-logistic-regression
- areas
- single-cell; biological-networks
- contexts
- research
- method types
- supervised_machine_learning
- reported name
- Gene-level features alone
- foundation model eligible
- false
- representation
- Logistic regression on mean expression, variance and dropout rate of the candidate target gene, 5-fold GroupKFold by perturbation.
- source locator
- Page 13 'Models and data', 'Attention extraction and edge score computation', 'Model coverage by analysis family'
- training setting
- Fitted under 5-fold GroupKFold by perturbation on each dataset
- access reuse
- Analysis code github.com/Biodyn-AI/biomechinterp-framework and Zenodo 10.5281/zenodo.18701417; not pinned to a commit in the article
- missing metadata
- version: reason: inapplicable; note: Defined in the article
Related records
- configuration of: Gene-level logistic regression (mean expression, variance, dropout rate)
- system: Gene-level logistic regression on Replogle genome-scale CRISPRi, K562, 280 evaluable perturbations
- system: Gene-level logistic regression on Replogle genome-scale CRISPRi, RPE1, 1,167 perturbations, confound battery