scHFBiMamba: a bidirectional Mamba autoencoder for cardiac disease-state classification from single-nucleus RNA-seq data
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The article reviews how cardiac imaging techniques such as echocardiography, CMR, and CT can be integrated with bulk, single-cell, and spatial transcriptomics to create joint representations that link imaging phenotypes to detailed molecular states. It outlines the representation needs of each modality, compares multimodal fusion strategies, and synthesizes pipelines for radiogenomics, spatial alignment, and image-based gene‑expression prediction, while discussing validation, limitations, and failure modes. The review highlights emerging spatial multiomic maps of human myocardium and atherosclerotic plaque, noting that cost, scalability, and tissue availability remain major barriers to large‑scale cardiovascular translation.
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