New framework fuses genomic language models with neuroimaging for disease diagnosis

Researchers have developed GeneFuse, a novel framework designed to integrate genomic data from pre-trained Genomic Language Models (GLMs) with neuroimaging features for improved disease diagnosis. This multimodal approach utilizes Genotype-Conditioned Feature Modulation (GCFM) to adjust image features based on genomic embeddings and Uncertainty-aware Genomic Residual Fusion (U-GRF) to dynamically combine genetic and imaging information. In tests for early cognitive decline and dementia screening, GeneFuse demonstrated strong performance, achieving AUROCs of 0.77 and 0.83 in an apolipoprotein E-centered setting, surpassing existing fusion methods. AI

IMPACT This framework could enhance the accuracy of diagnosing neurological diseases by leveraging advanced AI techniques to combine diverse biological data.

RANK_REASON The cluster contains an academic paper detailing a new framework and methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• apolipoproteins E
• GCFM
• Genomic Language Models
• Genotype-Conditioned Feature Modulation
• Neuroimaging
• U-GRF
• Uncertainty-aware Genomic Residual Fusion

• paper
• model release

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