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New AI models predict gene expression from histology images

By PulseAugur Editorial · [2 sources] ·

Researchers have developed new methods for predicting gene expression from histology images, offering a more cost-effective alternative to traditional spatial transcriptomics. One approach, GATE-ST, integrates text descriptions of genes with image data using cross-attention to improve prediction accuracy. Another method, CELLO, utilizes a single pathology foundation model forward pass and grid sampling to predict gene expression at the single-cell level, achieving significant speed-ups compared to previous methods. AI

IMPACT These methods could significantly reduce the cost and time associated with gene expression analysis, accelerating biological research and drug discovery.

RANK_REASON Two research papers published on arXiv detailing novel AI methods for spatial transcriptomics prediction.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New AI models predict gene expression from histology images

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Lucas Ni, Jian Luo, Wentao Huang, Chao Chen ·

    GATE-ST: Gene-Aware Text-image Encoder for Spatial Transcriptomics

    arXiv:2609.38690v1 Announce Type: new Abstract: Spatial transcriptomics enables spatially resolved gene expression analysis from slide-level images while preserving morphological features, providing valuable information for studying disease mechanisms and developing treatments. H…

  2. arXiv cs.CV TIER_1 English(EN) · Zijun Gao, Chunbin Gu, Jinxi Xiang, Xiangde Luo, Pheng-Ann Heng ·

    Towards Scalable Context-Aware Single-Cell Spatial Transcriptomics Prediction from Histology Images

    arXiv:2609.36429v1 Announce Type: new Abstract: Predicting gene expression from H&amp;E-stained histology images offers a scalable alternative to costly spatial transcriptomics, yet most existing methods operate at the spot level, where signals from multiple cells are aggregated …

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