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 experimentally validated TF-target interactions remain limited, making reliable inference challenging. Although graph neural networks have advanced GRN prediction, existing methods often rely on biologically unconstrained graph augmentation, such as random edge perturbation, and insufficiently control information transfer
The continuous advancements in AI and machine learning, particularly graph neural networks, are being applied to complex biological data, addressing limitations in current gene regulatory network inference methods.
Improved gene regulatory network inference is critical for understanding cell-state-specific transcriptional programs, enabling more precise biological and medical research and development.
This development represents a refinement in the methodology for inferring gene regulatory networks from single-cell RNA sequencing data, offering more accurate and biologically constrained predictions.
- · Bio-pharmaceutical companies
- · Synthetic biology researchers
- · AI/ML in life sciences sector
- · Genomic sequencing companies
- · Companies relying on less accurate GRN inference methods
More accurate gene regulatory networks could lead to better target identification for drug discovery.
Enhanced understanding of cellular processes might accelerate the development of new synthetic biology applications and biomanufacturing techniques.
The integration of advanced AI with biological data could reshape how we approach disease understanding and personalized medicine, leading to novel therapeutic strategies.
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Read at arXiv cs.AI