M$^3$-Gen：利用临床和影像数据对基因表达谱进行可解释的多模态生成
M$^3$-Gen: Interpretable Multimodal Generation of Gene Expression Profiles Using Clinical and Imaging Data
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中文总结 AI 辅助
研究旨在解决基因表达数据获取受限问题，提出M$^3$-Gen框架，通过对比学习从临床变量和图像中学习潜在表示，以生成生物学连贯的基因表达谱，在TCGA数据集上验证有效性，且具有内在可解释性。
中文摘要 AI 辅助
整合包括临床元数据、组织病理学图像和分子谱在内的异构生物医学数据对于全面理解疾病至关重要。然而，基因表达数据获取受高成本和隐私问题限制。我们提出多模态分子生成（M$^3$-Gen）框架，通过在组织病理学图像和临床元数据上对生成对抗网络进行条件设定来生成基因表达谱。它利用对比学习从临床变量和图像中学习统一潜在表示，利用两种模态的嵌入指导生成模型产生生物学上连贯的基因表达谱。在TCGA数据集上的评估表明其能生成现实且功能有意义的数据，且通过基于注意力机制整合多模态提供内在可解释性。
英文摘要
Integrating heterogeneous biomedical data, including clinical metadata, histopathology images, and molecular profiles, is crucial for comprehensive disease understanding. However, gene expression data acquisition remains constrained by high costs and privacy concerns, limiting its use in multimodal research and AI-driven applications. We present MultiModal Molecular Generation (M$^3$-Gen), a novel framework for the generation of gene expression profiles by conditioning a Generative Adversarial Network on histopathology images and clinical metadata. M$^3$-Gen learns a unified latent representation from the clinical variables and the images, leveraging contrastive learning, and exploits the embeddings of the two modalities to guide a generative model in producing biologically coherent gene expression profiles. Evaluations on the TCGA dataset demonstrate that M$^3$-Gen generates realistic and functionally meaningful gene expression data. Importantly, by integrating multiple modalities in an attention-based mechanism, M$^3$-Gen provides intrinsic explainability: it allows the identification of which regions of the histopathology images most strongly influenced the generation of specific gene expression profiles, making the model's decisions interpretable by design.
发表机构
- DEIB - Dipartimento Elettronica, Informazione e Bioingegneria, Politecnico di Milano（米兰理工大学电子、信息与生物工程系）
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