Sethi, T.; Anand, A.; Pratiti, M.; Ali, S. Y.; Kamra, S.; Verma, S.; Singh, S.; Bajaj, T.

Identifying robust gene expression signatures from transcriptomic studies with small sample sizes remains one of the most persistent challenges in computational biology. Gene expression datasets have thousands of features but only a handful of biological samples. This presents the classic p >> n imbalance, which limits statistical power and makes it difficult to discover reliable biomarkers. In imaging, generative models such as GANs, VAEs, and diffusion models have demonstrated promising applications in data augmentation, but their usefulness for omics data has not been systematically tested. More importantly, no existing framework integrates synthetic data generation, stability-aware signature discovery, and multi-source biological validation into a single pipeline. In this work, we present GeneLift, with the hypothesis that a computational pipeline of generative data augmentation, stability testing, and evaluating biological evidence will aid novel gene-signature discovery in small-cohort transcriptomic studies. We tested this hypothesis across 36 microarray datasets covering five diseases: sepsis, breast cancer, ovarian cancer, tuberculosis, and diabetes. A component-wise testing of GeneLift revealed that Gaussian Mixture Models (GMMs) outperformed deep generative approaches and faithfully reproduced gene-level distributions. By a novel approach of titrating the level of augmentation, we identified biologically meaningful gene candidates that did not appear in the original, underpowered analyses. We also developed BayesScore, a Bayesian posterior probability of gene-disease association computed from PubMed co-occurrence, which both recovers well-characterised disease genes missed by standard differential expression and surfaces candidates whose disease relevance was independently confirmed in subsequent publications, with lead times of up to 18 years between the source dataset and the first disease-specific citation. GeneLift is freely available at tavlab-iiitd/GeneLift.