GeneQuery: Generalized Gene Expression Prediction from Histology Images via Image-Gene QA
Xiong, Ying Liu, Linjing Cui, Yufei Wu, Shangyu Liu, Xue Chan, Antoni B Xue, Chun Jason
Xiong, Ying
Liu, Linjing
Cui, Yufei
Wu, Shangyu
Liu, Xue
Chan, Antoni B
Xue, Chun Jason
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Department
Machine Learning
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Conference proceeding
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Abstract
Gene expression profiling provides profound insights into molecular mechanisms, but its time-consuming and costly nature often presents significant challenges. Recent advancements have utilized histological images to predict spatially resolved gene expression profiles. The gene prediction problem has two main characteristics, i.e., spatial heterogeneity and gene interdependency. Existing works only focus on addressing spatial heterogeneity, ignoring the importance of relationships between genes. To address the above limitation, this paper presents GeneQuery, which aims to solve this gene expression prediction task in a question-answering (QA) manner for better generality and flexibility. Specifically, GeneQuery takes gene meta-information as queries and whole-slide images as contexts and then predicts the queried gene expression values. GeneQuery learns to dynamically fuse spatial image features with gene semantics, and uses an attention mechanism to explicitly capture the spatial information and a shared regressor to implicitly capture the gene relationship. A variant of GeneQuery can use the attention mechanism to capture gene relationships for more complex tissue cases. This QA-based reformulation also grants the model the ability to generalize, enabling the prediction of unseen gene expression without requiring model retraining. Comprehensive experiments on spatial transcriptomics datasets show that the proposed GeneQuery outperforms existing state-of-the-art methods on known and unseen genes. More results also demonstrate that GeneQuery can help analyze the tissue structure.
Citation
Y. Xiong, L. Liu, Y. Cui, S. Wu, X. Liu, A.B. Chan, C.J. Xue, "GeneQuery: Generalized Gene Expression Prediction from Histology Images via Image-Gene QA," 2026, pp. 5738-5745.
Source
Conference
2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Keywords
31 Biological Sciences, 3102 Bioinformatics and Computational Biology
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Source
2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Publisher
IEEE