<p>The evolution of transcriptomic technologies requires effective translation between legacy and modern platforms to fully leverage historical data. We present GANomics, a generative adversarial network (GAN) framework that enables bidirectional translation between microarray and RNA-seq data using a small number of samples profiled by both technologies. By integrating paired and unpaired samples through a pair-aware feedback loss, GANomics enforces one-to-one transcript mappings while preserving global gene expression distributions. Applied to a neuroblastoma cohort (<i>n</i> = 498; 10,042 genes) and benchmarked against six alternative methods, GANomics achieved high per-sample correlations ( &gt; 0.96) between real and synthetic data with as few as ten paired profiles. With fifty paired profiles, it accurately recapitulated differential expression, maintained pathway-level rankings, and enabled a cross-platform classifier to transfer comparable to real data. Consistent performance across five additional benchmark datasets confirmed its robustness. By bridging legacy and contemporary transcriptomic data while retaining biological consistency, GANomics facilitates scalable data integration for biomarker reuse and the expansion of transcriptomic resources in clinical applications.</p>