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Perturbation-target prediction from attention-derived edge scores (Kendiukhov 2026, Objective B)

For each CRISPR perturbation, score every other gene by an edge score to the perturbed gene (attention, Spearman correlation) or by a gene-level statistic (variance, mean expression, dropout rate), and measure AUROC for classifying the differentially expressed targets. Incremental-value tests add edge scores to a gene-level logistic regression.

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Stable ID: gfm-guides-20261014-kendiukhov2026-benchmark-perturbation-target-prediction

areas
single-cell; biological-networks
contexts
research
entity level
suite
version
BMC Genomics 27:634 (2026)
task
Predict which genes are differentially expressed after a CRISPRi or CRISPRa perturbation
scope note
Replogle genome-scale CRISPRi (K562, RPE1) and a K562 CRISPRa dataset; T cell and iPSC neuron sets have n = 7 perturbations and print no AUROC values.
source locator
Pages 4 and 7-10, 'Results'; pages 13-14, 'Methods'
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