Decoding Phenotypes: A Framework for Fusing Genomic Language Models and Neuroimaging

2026-08-09

AI Summary

Cross-modal heterogeneity poses a significant challenge to the effective integration of genomic and neuroimaging data, thereby limiting precise diagnosis of neurological disorders. To address this issue, this work proposes GeneFuse, a novel multimodal learning framework that, for the first time, incorporates a pretrained genomic language model (GLM) into imaging-genomics fusion tasks. GeneFuse preserves sequence context through genotype-conditioned feature modulation (GCFM) and dynamically adjusts the contribution of genomic features via an uncertainty-aware residual fusion mechanism. Evaluated under APOE-stratified scenarios, the proposed method achieves AUROC scores of 0.77 and 0.83 on NC vs. MCI and NC vs. AD classification tasks, respectively, substantially outperforming existing fusion approaches.

Abstract

Neuroimaging and genetic testing are two important clinical references for nervous system diseases, offering complementary diagnostic information. However, integrating genomic and neuroimaging data for precise disease diagnosis is challenging due to cross-modality heterogeneity. Existing imaging-genetics approaches mainly encode genetic information as hard-coded labels, which lose the local sequence context around disease-associated variants. To address this limitation, we propose GeneFuse, a multimodal learning framework that aligns genetic representations from pre-trained Genomic Language Models (GLMs) with features extracted from images. GeneFuse integrates two components: (1) Genotype-Conditioned Feature Modulation (GCFM), a FiLM-inspired module that uses genomic embeddings to modulate image feature maps; and (2) Uncertainty-aware Genomic Residual Fusion (U-GRF), a fusion strategy that uses imaging-derived predictive uncertainty to gate the contribution of genotypic features. We evaluate GeneFuse on early cognitive decline identification (NC vs. MCI) and dementia screening (NC vs. AD). In the APOE-centered setting, GeneFuse achieves AUROCs of 0.77 and 0.83, outperforming existing imaging-genetics fusion methods. These results indicate that GLM-derived genomic embeddings provide additional information to imaging.

Problem

• imaging-genetics integration
• cross-modality heterogeneity
• genomic language models
• neuroimaging
• precision diagnosis

Innovation

• Genomic Language Models
• Multimodal Fusion
• Neuroimaging
• Feature Modulation
• Uncertainty-aware Fusion