# STRAND: Sequence-Conditioned Transport for Single-Cell Perturbations

arXiv: https://arxiv.org/abs/2602.10156 · HTML: https://mlsanity.com/paper/2602.10156/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 75 references that are in the map.
- First posted on arXiv 10 Feb 2026.

## Builds on (most cited first, 20 of 20)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics](https://mlsanity.com/paper/2002.04461/) (2020)
- [Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution](https://mlsanity.com/paper/2204.13545/) (2022)
- [Modelling Cellular Perturbations with the Sparse Additive Mechanism Shift Variational Autoencoder](https://mlsanity.com/paper/2311.02794/) (2023)
- [PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis](https://mlsanity.com/paper/2408.10609/) (2024)
- [scDiffusion: conditional generation of high-quality single-cell data using diffusion model](https://mlsanity.com/paper/2401.03968/) (2024)
- [Predicting Cellular Responses with Variational Causal Inference and Refined Relational Information](https://mlsanity.com/paper/2210.00116/) (2022)
- [Season combinatorial intervention predictions with Salt & Peper](https://mlsanity.com/paper/2404.16907/) (2024)
- [Contextualizing biological perturbation experiments through language](https://mlsanity.com/paper/2502.21290/) (2025)
- [Hierarchical Refinement: Optimal Transport to Infinity and Beyond](https://mlsanity.com/paper/2503.03025/) (2025)
- [TxPert: Leveraging Biochemical Relationships for Out-of-Distribution Transcriptomic Perturbation Prediction](https://mlsanity.com/paper/2505.14919/) (2025)
- [Multi-modal Transfer Learning between Biological Foundation Models](https://mlsanity.com/paper/2406.14150/) (2024)
- [Removing Biases from Molecular Representations via Information Maximization](https://mlsanity.com/paper/2312.00718/) (2023)
- [DART-Eval: A Comprehensive DNA Language Model Evaluation Benchmark on Regulatory DNA](https://mlsanity.com/paper/2412.05430/) (2024)
- [scPPDM: A Diffusion Model for Single-Cell Drug-Response Prediction](https://mlsanity.com/paper/2510.11726/) (2025)
- [Learning Cross-Domain Representations for Transferable Drug Perturbations on Single-Cell Transcriptional Responses](https://mlsanity.com/paper/2412.19228/) (2024)
- [Towards generalizable single-cell perturbation modeling via the Conditional Monge Gap](https://mlsanity.com/paper/2504.08328/) (2025)
- [Latent Causal Diffusions for Single-Cell Perturbation Modeling](https://mlsanity.com/paper/2601.15341/) (2026)
- [Interpretable Perturbation Modeling Through Biomedical Knowledge Graphs](https://mlsanity.com/paper/2512.22251/) (2025)

## Built on by (most cited first, 4 of 4)

- [Central Dogma Transformer III: Interpretable AI Across DNA, RNA, and Protein](https://mlsanity.com/paper/2603.23361/) (2026)
- [Querying Counterfactuals on Tissue Graphs with Supervised Disentanglement](https://mlsanity.com/paper/2606.08493/) (2026)
- [PerturbMap: Cross-Context Transfer of Single-Cell Perturbation Responses](https://mlsanity.com/paper/2607.28090/) (2026)
- [GenoMorph: Pathway-Grounded Genomic Disease Reasoning via Adaptive Latent Computation](https://mlsanity.com/paper/2609.34079/) (2026)

## Abstract (arXiv)

Predicting how genetic perturbations change cellular state is a core problem for building controllable models of gene regulation. Perturbations targeting the same gene can produce different transcriptional responses depending on their genomic locus, including different transcription start sites and regulatory elements. Gene-level perturbation models collapse these distinct interventions into the same representation. We introduce STRAND, a generative model that predicts single-cell transcriptional responses by conditioning on regulatory DNA sequence. STRAND represents a perturbation by encoding the sequence at its genomic locus and uses this representation to parameterize a conditional transport process from control to perturbed cell states. Representing perturbations by sequence, rather than by a fixed set of gene identifiers, supports zero-shot inference at loci not seen during training and expands inference-time genomic coverage from ~1.5% for gene-level single-cell foundation models to ~95% of the genome. We evaluate STRAND on CRISPR perturbation datasets in K562, Jurkat, and RPE1 cells. STRAND improves discrimination scores by up to 33% in low-sample regimes, achieves the best average rank on unseen gene perturbation benchmarks, and improves transfer to novel cell lines by up to 0.14 in Pearson correlation. Ablations isolate the gains to sequence conditioning and transport, and case studies show that STRAND resolves functionally alternative transcription start sites missed by gene-level models.