New Methods Unlock Generalizable Regulatory Knowledge in Single-Cell Foundation Models for GRN Inference
Researchers have introduced a novel approach to improve Gene Regulatory Network (GRN) inference using single-cell Foundation Models (scFMs). While scFMs were expected to revolutionize this field, their performance has been limited because standard pre-training objectives fail to capture latent regulatory signals. To address this, the team developed a new GRN generalization benchmark that evaluates predictions on unseen genes and datasets, leveraging the zero-shot capabilities of scFMs. Additionally, they proposed two innovative methods, Virtual Value Perturbation and Gradient Trajectory, designed to distill implicit regulatory information from scFMs into highly generalizable inter-gene features. Extensive experiments demonstrate that this new framework significantly outperforms existing methods. This work establishes a new paradigm for utilizing scFMs in universal GRN inference, offering enhanced tools for understanding complex cellular mechanisms through single-cell transcriptomic data. The findings were published on arXiv in May 2026, highlighting significant advancements in computational biology and machine learning applications in genomics.
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New Methods Unlock Generalizable Regulatory Knowledge in Single-Cell Foundation Models for GRN Inference
Researchers have introduced a novel approach to improve Gene Regulatory Network (GRN) inference using single-cell Foundation Models (scFMs). While scFMs were expected to revolutionize this field, their performance has been limited because standard pre-training objectives fail to capture latent regulatory signals. To address this, the team developed a new GRN generalization benchmark that evaluates predictions on unseen genes and datasets, leveraging the zero-shot capabilities of scFMs. Additionally, they proposed two innovative methods, Virtual Value Perturbation and Gradient Trajectory, designed to distill implicit regulatory information from scFMs into highly generalizable inter-gene features. Extensive experiments demonstrate that this new framework significantly outperforms existing methods. This work establishes a new paradigm for utilizing scFMs in universal GRN inference, offering enhanced tools for understanding complex cellular mechanisms through single-cell transcriptomic data. The findings were published on arXiv in May 2026, highlighting significant advancements in computational biology and machine learning applications in genomics.
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