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New GC-MoE model predicts cell gene expression from histology

Researchers have developed GC-MoE, a novel method for estimating gene expression in individual cells from histology images. This approach uses a Mixture-of-Experts model guided by genomic data to predict cell-type probabilities and gene expression. The system incorporates cell-type-specific predictors and attention mechanisms to capture gene programs and neighboring cell context, showing improved performance over existing methods. AI

IMPACT Introduces a new computational model for biological research, potentially improving the efficiency of gene expression analysis.

RANK_REASON The cluster contains a research paper detailing a new computational model for a specific scientific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Kaito Shiku, Ahtisham Fazeel Abbasi, Ryoma Bise, Yuichiro Iwashita, Kazuya Nishimura, Andreas Dengel, Muhammad Nabeel Asim ·

    GC-MoE: Genomics-Guided Cell-Type-Specific Mixture of Experts for Histology-Based Single-Cell Spatial Transcriptomics

    arXiv:2606.02424v1 Announce Type: cross Abstract: Histology-based single-cell spatial transcriptomics (ST) estimation aims to predict gene expression for individual cells from histopathological images and cell locations, reducing the need for costly single-cell ST measurements. U…