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Histopathology images capture tissue morphology, while spatial transcriptomics (ST) provides spatially resolved gene expression, offering complementary molecular insights. However, acquiring ST data is costly and time-consuming, limiting its practical use. To address this, we propose HAGE (Hierarchical Alignment Gene-Enhanced), a framework that enhances pathology representation learning by predicting gene expression directly from histological images and integrating molecular context into the pathology model. HAGE leverages gene-type embeddings, which encode relationships among genes, guiding the model in learning biologically meaningful expression patterns. To further improve alignment between histology and gene expression, we introduce a hierarchical clustering strategy that groups image patches based on molecular and visual similarity, capturing both local and global dependencies. HAGE consistently outperforms existing methods across six datasets. In particular, on the HER2+ breast cancer cohort, it significantly improves the Pearson correlation coefficient by 8.0% and achieves substantial reductions in mean squared error and mean absolute error by 18.1% and 38.0%, respectively. Beyond gene expression prediction, HAGE improves downstream tasks, such as patch-level cancer classification and whole-slide image diagnostics, demonstrating its broader applicability. To the best of our knowledge, HAGE is the first framework to integrate gene co-expression as prior knowledge into a pathology image encoder via a cross-attention mechanism, enabling more biologically informed and accurate pathology representations. https://github.com/uta-smile/gene_expression. 

Exploring the functions of genes and gene products is crucial to a wide range of fields, including medical research, evolutionary biology, and environmental science. However, discovering new functions largely relies on expensive and exhaustive wet lab experiments. Existing methods of automatic function annotation or prediction mainly focus on protein function prediction with sequence, 3D-structures or protein family information. In this study, we propose to tackle the gene function prediction problem by exploring Gene Ontology graph and annotation with BERT (GoBERT) to decipher the underlying relationships among gene functions. Our proposed novel function prediction task utilizes existing functions as inputs and generalizes the function prediction to gene and gene products. Specifically, two pre-train tasks are designed to jointly train GoBERT to capture both explicit and implicit relations of functions. Neighborhood prediction is a self-supervised multi-label classification task that captures the explicit function relations. Specified masking and recovering task helps GoBERT in finding implicit patterns among functions. The pre-trained GoBERT possess the ability to predict novel functions for various gene and gene products based on known functional annotations. Extensive experiments, biological case studies, and ablation studies are conducted to demonstrate the superiority of our proposed GoBERT.