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New scGPL framework enhances gene regulatory network inference

By PulseAugur Editorial · [1 sources] ·

Researchers have developed scGPL, a novel framework designed to improve the inference of gene regulatory networks (GRNs) from single-cell RNA sequencing data. This approach integrates graph-based and pairwise learning methods, addressing the limitations of each by considering both global topological structures and local TF-target gene interactions. Experiments show scGPL significantly outperforms existing methods, achieving up to a 13.7% improvement on benchmark datasets. The framework's code is publicly available on GitHub for reproducibility. AI

IMPACT Enhances biological data analysis capabilities, potentially accelerating discoveries in genomics and systems biology.

RANK_REASON The cluster describes a new research paper published on arXiv detailing a novel 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 scGPL framework enhances gene regulatory network inference

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The cluster describes a new research paper published on arXiv detailing a novel 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) · Hang Li, Jiaxin Qi, Yan Cui, Yuhua Zheng, Jianqiang Huang ·

    Bridging Graph and Pairwise Learning for Gene Regulatory Network Inference from scRNA-seq Data

    arXiv:2611.13801v1 Announce Type: cross Abstract: Motivation: Gene regulatory networks (GRNs) capture the underlying interactions through which transcription factors (TFs) regulate genes. Based on gene expression data, existing GRN inference approaches generally fall into two cat…

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