Stable-Shift: Biologically Structured Prediction of Transcriptional Responses to Unseen Gene Perturbations
arXiv:2606.24940v1 Announce Type: cross Abstract: Predicting transcriptional responses to genetic perturbations could reduce the experimental burden of functional genomics, but extrapolation to genes that were never perturbed during training remains difficult. We present Stable-Shift, a structured method for estimating unseen-gene responses. 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 combi
The continuous advancements in AI and machine learning techniques are increasingly being applied to complex biological problems, driven by larger datasets and improved computational power.
This development significantly enhances the ability to predict biological responses to genetic interventions, potentially accelerating drug discovery, disease understanding, and the engineering of biological systems.
The ability to accurately predict transcriptional responses to unseen gene perturbations reduces the reliance on laborious and expensive experimental validation for every single gene modification.
- · Biotechnology and pharmaceutical companies
- · Genomic research institutions
- · Synthetic biology companies
- · AI/ML in life sciences
- · Traditional high-throughput screening methods
- · Companies relying solely on empirical trial-and-error in genetic engineering
More efficient and targeted gene editing and drug development processes.
A faster design-build-test cycle in synthetic biology, leading to novel bio-products and therapies.
Enhanced ability to engineer organisms for specific functions, impacting agriculture, materials science, and environmental remediation.
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