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New Transformer Model Enhances Gene Regulatory Network Inference

By PulseAugur Editorial · [1 sources] ·

Researchers have developed EpiAwareNet, a novel framework utilizing multi-omic Transformers to infer gene regulatory networks (GRNs) from single-cell data. This method integrates transcriptomic and chromatin accessibility information, overcoming challenges like data sparsity and reliance on fixed gene-peak links. EpiAwareNet employs a gene-peak cross-attention module for adaptive signal aggregation and incorporates a prior GRN from bulk data as weak supervision, enhancing biological plausibility and improving reconstruction accuracy over existing methods. AI

IMPACT This new method could advance our understanding of cell regulation and disease by improving the accuracy of gene regulatory network reconstruction.

RANK_REASON The cluster contains a research paper detailing a new method for biological network inference. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Transformer Model Enhances Gene Regulatory Network Inference

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The cluster contains a research paper detailing a new method for biological network inference. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Tianyang Xu, Tianci Liu, Niraj Rayamajhi, Ryan Patrick, Kranthi Varala, Ying Li, Jing Gao ·

    Prior-Guided Multi-Omic Transformers for Single-Cell Gene Regulatory Network Inference

    arXiv:2606.00685v1 Announce Type: new Abstract: Gene regulatory networks (GRNs) capture transcription factor-target interactions and are central to understanding cell-state regulation and disease. Reconstructing GRNs from paired single-cell transcriptomic and chromatin accessibil…

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