Stable-Shift: Predicting Transcriptional Responses of Unseen Gene Perturbations Using Graph Neural Networks with Biological Priors

Predicting transcriptional responses to genetic perturbations could reduce the experimental burden of functional genomics, but extrapolation to genes never perturbed during training remains difficult.

Stable-Shift aggregates single-cell measurements into perturbation-level expression shifts, fits a low-rank response basis using training perturbations only, and predicts an unseen gene’s coordinates in that basis from biological context. The context combines STRING interactions, network structure, control-cell expression statistics, and Gene Ontology annotations; graph convolution integrates these inputs.

On the supplied K562 Perturb-seq benchmark, Stable-Shift obtained 0.592 cosine similarity, compared with 0.569 for GEARS, together with higher Spearman correlation and top-gene precision among the evaluated methods. Its mean cosine similarity over five unseen-gene splits was 0.589 ± 0.008. The same ordering was observed in the supplied graph-aware, residualized, gene-space, and Norman-dataset comparisons.

These results support further study of biologically structured latent-response prediction, while the lower gene-space accuracy and sensitivity to sparse graph neighborhoods limit the scope of the present conclusions.

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Citation

@inproceedings{dip2026stableshift,
  author    = {Dip, Sajib Acharjee and Zhang, Liqing},
  title     = {Stable-Shift: Predicting Transcriptional Responses of Unseen Gene Perturbations Using Graph Neural Networks with Biological Priors},
  booktitle = {Proceedings of the 17th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics},
  year      = {2026},
  articleno = {101},
  numpages  = {6},
  doi       = {10.1145/3807503.3820871}
}

Recommended citation: Dip, Sajib Acharjee, and Liqing Zhang. (2026). "Stable-Shift: Predicting Transcriptional Responses of Unseen Gene Perturbations Using Graph Neural Networks with Biological Priors." Proceedings of ACM BCB 2026, Article 101, 6 pages.
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