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New AI models translate histology images to spatial transcriptomics data

By PulseAugur Editorial · [2 sources] ·

Two new research papers, Path2ST and PaSTel, introduce advanced methods for translating histological images into spatial transcriptomic data. Path2ST utilizes a hierarchical approach, grounding cross-modal translation in cell and tissue structures to generate accurate gene expression profiles. PaSTel enhances this by incorporating multi-scale biological priors, including TF-IDF gene selection, KEGG pathways, and spatial clustering, to create more informative and transferable representations for spatial transcriptomics. AI

IMPACT These new methods could accelerate biological research by enabling more cost-effective and detailed analysis of gene expression within tissue structures.

RANK_REASON Two academic papers published on arXiv introducing new methods for spatial transcriptomics.

Read on arXiv cs.AI →

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

New AI models translate histology images to spatial transcriptomics data

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Ruochen Liu, Wei Lou ·

    Path2ST: Hierarchical Cell-Tissue Grounded Cross-Modal Translation for Spatial Transcriptomics

    arXiv:2608.14710v1 Announce Type: cross Abstract: Predicting spatial gene expression from hematoxylin and eosin (H\&amp;E)-stained images offers a cost-effective alternative to spatial transcriptomics (ST). However, existing methods treat H\&amp;E images as generic visual inputs …

  2. arXiv cs.AI TIER_1 English(EN) · Azim Dehghani Amirabad, Junchao Zhu, Pushpak Pati, Walid Abdelmoula, Tommaso Mansi, Rui Liao ·

    PaSTel: Anchoring Histology in Spatial Transcriptomics via Multi-Scale Hierarchical Bio-Prior Contrastive Pretraining

    arXiv:2608.14924v1 Announce Type: cross Abstract: Spatial transcriptomics (ST) links tissue morphology with molecular programs, motivating multimodal pretraining methods that align histology images with gene expression. However, existing approaches suffer from two key limitations…

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