Gene-level logistic regression plus correlation edge (Kendiukhov 2026)
Logistic regression on mean expression, variance and dropout rate of the candidate target gene plus the absolute Spearman correlation between the perturbed gene and the candidate, 5-fold GroupKFold by perturbation.
Overview
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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 difference (Gene-level features plus correlation edge minus gene-level features only (gene-level logistic regression); 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 difference (Gene-level features plus correlation edge minus gene-level features only (gene-level logistic regression); cross-validated AUROC over perturbation-gene pairs, 5-fold GroupKFold by perturbation) |
|---|---|---|
| Gene-level logistic regression plus correlation edge (Kendiukhov 2026)Evaluation | Replogle genome-scale CRISPRi, RPE1, 1,167 perturbations, confound battery (Kendiukhov 2026) | −0.0006 (best in column) |
| Gene-level logistic regression plus correlation edge (Kendiukhov 2026)Evaluation | Replogle genome-scale CRISPRi, K562, 280 evaluable perturbations (Kendiukhov 2026) | −0.002 |
All 2 result rows with coverage, uncertainty and sources
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How it works, versions and access
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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-lr-plus-correlation
- areas
- single-cell; biological-networks
- contexts
- research
- method types
- supervised_machine_learning
- reported name
- gene-level plus correlation
- foundation model eligible
- false
- representation
- Gene-level features of the candidate target plus one absolute Spearman correlation edge score
- 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
- source locator
- Page 8 'No incremental pairwise value on the perturbation-target prediction task', paragraph 1; page 16 'Confound decomposition'
- 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 plus correlation edge on Replogle genome-scale CRISPRi, K562, 280 evaluable perturbations
- system: Gene-level logistic regression plus correlation edge on Replogle genome-scale CRISPRi, RPE1, 1,167 perturbations, confound battery