M$^3$-Gen: Interpretable Multimodal Generation of Gene Expression Profiles Using Clinical and Imaging Data
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 N
Record details
Published: 23 July 2026
Source: arXiv cs.AI
Category: Research
Topics: Privacy · Healthcare
Retrieved: 25 July 2026
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ethics.ai (23 July 2026), “M$^3$-Gen: Interpretable Multimodal Generation of Gene Expression Profiles Using Clinical and Imaging Data,” evidence record 13473, https://ethics.ai/record/13473 (originally published by arXiv cs.AI).
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