Stable-Shift fits a low-rank transcriptional response basis from training perturbations and uses graph convolution on biological context (STRING, network, expression stats, GO) to predict coordinates for unseen genes, reaching 0.592 cosine similarity on K562 Perturb-seq data versus 0.569 for GEARS.
Llm4cell: A survey of large language and agentic models for single-cell biology.arXiv preprint arXiv:2510.07793,
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scLLM-DSC integrates a knowledge-driven semantic view from gene priors with a structure-aware topological view through cross-modal contrastive learning and reports higher clustering accuracy than eleven baselines on scRNA-seq data.
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Stable-Shift: Biologically Structured Prediction of Transcriptional Responses to Unseen Gene Perturbations
Stable-Shift fits a low-rank transcriptional response basis from training perturbations and uses graph convolution on biological context (STRING, network, expression stats, GO) to predict coordinates for unseen genes, reaching 0.592 cosine similarity on K562 Perturb-seq data versus 0.569 for GEARS.
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scLLM-DSC: LLM-Knowledge Enhanced Cross-Modal Deep Structural Clustering for Single-Cell RNA Sequencing
scLLM-DSC integrates a knowledge-driven semantic view from gene priors with a structure-aware topological view through cross-modal contrastive learning and reports higher clustering accuracy than eleven baselines on scRNA-seq data.