Artificial Intelligence as a Conceptual Lens on Gene Latency and Genomic Memory

Genomic regulation is typically interpreted through observable molecular states such as gene expression, chromatin accessibility and epigenetic modifications. However, biological systems also contain large reservoirs of genomic information that remain transcriptionally inactive for extended periods while retaining the capacity to influence future regulatory behaviour. This phenomenon, referred to here as gene latency, suggests that genomes may preserve forms of biological memory beyond currently expressed molecular states. Recent advances in artificial intelligence—particularly transformer-based architectures—demonstrate how complex systems can encode structured information within latent representational spaces that influence outputs without continuous activation (Vaswani et al., 2017; Brown et al., 2020). In this study, we propose a conceptual framework that interprets gene latency as a form of genomic memory using principles derived from latent representation learning in artificial intelligence. By aligning concepts from systems biology, epigenetics and machine learning, we outline theoretical and computational perspectives for identifying latent regulatory potential within genomic systems. This framework suggests that genomes may contain distributed reservoirs of regulatory capacity shaped by developmental history and environmental exposure. Integrating artificial intelligence with genomic theory may therefore enable new approaches for modelling latent regulatory states and predicting transitions from genomic latency to activation.