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New BRIDGE Framework Enhances Gene Regulatory Network Inference

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework called BRIDGE to improve the inference of gene regulatory networks from single-cell RNA sequencing data. This method addresses challenges posed by noisy and sparse data by employing contrastive learning and heterogeneous gated encoding to adaptively regulate information transfer between genes and cells. Experiments demonstrate that BRIDGE achieves state-of-the-art performance, outperforming existing methods like GCLink, particularly in few-shot transfer scenarios across different cell types. A case study on hESC data further validated the biological relevance of BRIDGE's predictions. AI

RANK_REASON The cluster contains a research paper detailing a new computational framework for biological data analysis. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.AI →

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New BRIDGE Framework Enhances Gene Regulatory Network Inference

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The cluster contains a research paper detailing a new computational framework for biological data analysis. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ziyang Dong, Shanwen Tan, Hengchuang Yin, Wei Liu, Yifan Wang, Siyu Yi, Jiancheng Lv, Wei Ju ·

    BRIDGE: Biological Evidence Refinement and Heterogeneous Dynamic Gating for Gene Regulatory Networks

    arXiv:2606.14734v1 Announce Type: cross Abstract: Motivation: Gene regulatory network inference from single-cell RNA sequencing (scRNA-seq) data is important for uncovering cell-state-specific transcriptional programs. However, scRNA-seq measurements are sparse and noisy, and exp…

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