Formulating hypotheses about gene-disease associations requires logical inference from prior data, followed by a laborious literature review. AI models trained on curated datasets (e.g., GWAS Catalog) can suggest SNP-disease links, but validating these predictions still demands manual evidence extraction. To streamline this process, we present GENET (Genomic Evidence Network Exploration Tool), an AI-enhanced, end-to-end visual analytics workflow applied to Age-Related Macular Degeneration (AMD). GENET comprises four sequential steps: (1) biomedical network analysis: a dual-encoder neural model identifies genes or SNPs associated with a target disease and vice versa; (2) literature evidence mining pipeline: a pipeline retrieves relevant papers and, using large language models, extracts biomedical entities and relations; (3) clustering: embeddings from pre-trained biomedical language models (BioBERT, BioLinkBERT) are clustered to group related concepts; (4) interactive visualizations: clusters and their networks are visualized with interactive features for hypothesis testing and insight generation. The workflow enables iterative hypothesis formulation and evidence validation, uncovering novel associations. GENET is open-sourced (
https://github.com/BiomedSciAI/genet) and available for demonstration at
https://genet.pythonanywhere.com.
