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In Silico Perturbation Prediction

Definition
AI-generated

In silico perturbation prediction forecasts how a cell’s molecular profile would change under a genetic or chemical perturbation not observed in the training experiment—using deep generative models, graph networks, optimal transport, or foundation models trained on atlas and perturbation compendia (e.g. scPerturb).

Topics
Single-cell analysis
Related
Causal Machine Learning for Single-Cell Genomics Deep Generative Model Gene Regulatory Network Inference

Why it matters in GWAS

It offers a scalable way to prioritize GWAS genes by simulating knockdown or overexpression effects on disease-relevant programs in reference cell types, complementing Perturb-seq when experiments are infeasible—subject to validation and calibration concerns.

Example usage

"The methods include In Silico Perturbation Prediction to support interpretation of the main results."

Causal Machine Learning for Single-Cell Genomics Deep Generative Model Gene Regulatory Network Inference Optimal Transport in Single-Cell Omics Perturb-seq scPerturb Single-Cell Foundation Model Single-Cell Perturbation Genomics
Related-term network

Click any node to open that term page.

Node order. The current term is first. Next come every page linked from Related terms, in the same top-to-bottom order as that list. Then new terms appear in two-hop layers: for each of those direct neighbors (in that same order), their related terms are appended if not already listed. A third pass does the same for two-hop nodes. The walk stops when a fixed maximum number of nodes is reached.

References

  • Roohani Y, et al. (2024). Predicting transcriptional outcomes of novel multigene perturbations with GEARS. Nat Biotechnol.
  • Ji Y, Lotfollahi M, Wolf FA, Theis FJ. (2021). Machine learning for perturbational single-cell omics. Cell Syst.
  • Dimitrov D, Schrod S, Rohbeck M, et al. (2026). Interpretation, extrapolation and perturbation of single cells. Nat Rev Genet. https://doi.org/10.1038/s41576-025-00920-4
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