发表机构
Ridge High School; Stony Brook University（岭高中; 纽约州立大学石溪分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
GATE-ST通过引入文本编码器融合基因描述与图像特征，提升空间转录组学预测性能，降低时间和成本。
AI 中文摘要
空间转录组学能够从切片级图像中进行空间分辨的基因表达分析，同时保留形态学特征，为研究疾病机制和开发治疗方法提供了宝贵信息。然而，空间基因表达谱分析通常需要昂贵且耗时的测试。虽然现有的基于图像的预测优化主要围绕位置嵌入的引入和进一步的图像改进，基于文本的优化仍相对未被探索。我们提出了GATE-ST，它将基于文本的输入整合到基于图像的空间基因表达预测中。通过这种方法，生成的基因文本描述被用于改善空间转录组学预测结果。基因摘要通过文本编码器处理，生成嵌入，并通过交叉注意力层与图像嵌入整合，以与形态学特征对齐。我们通过将性能与随机基因嵌入及多种其他图像-文本融合架构进行基准测试，证明了此类文本输入的有效性，并表明GATE-ST优于这些替代方案。我们的结果证明了GATE-ST在病理成像中的有效性，这可能大大减少准确空间转录组学预测的时间和成本，证明了文本引导的空间基因表达预测的潜力。
英文摘要
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. However, spatial gene expression profiling typically requires expensive and time-consuming tests. While existing image-based prediction optimizations mostly revolve around including positional embeddings and further image-based changes, text-based optimizations remain relatively unexplored. We present GATE-ST, which incorporates text-based inputs into image-based spatial gene expression predictions. With this approach, generated text descriptions of genes are utilized to better spatial transcriptomics prediction results. Gene summaries are put through a text encoder, generating embeddings that integrate with image embeddings through cross-attention layers to align with morphological features. We demonstrate the effectiveness of such text inputs by benchmarking performance against random gene embeddings and multiple other image-text fusion architectures, and show that GATE-ST outperforms these alternatives. Our results demonstrate the effectiveness of GATE-ST in pathology imaging, which may greatly reduce the time and cost of accurate spatial transcriptomic predictions, proving the potential of text-guided spatial gene expression prediction.
