Memory-Augmented Multi-Omics Learning for Predicting Exercise-Induced Transcriptomic and Splicing Responses in Human Skeletal Muscle

Abstract

Predicting individualized molecular responses to acute and chronic exercise is a grand challenge that sits at the confluence of systems genomics, artificial intelligence, and precision health. Human skeletal muscle remodeling involves coordinated transcriptomic and alternative splicing programs that unfold over multiple temporal scales, and these programs are modulated by genetic variation, epigenetic state, and environmental context. Traditional machine learning models, constrained by fixed-size context windows and static input representations, are ill-suited for capturing the nonstationary and interdependent dynamics of multi-omics time courses. This paper presents a system-level analysis of memory-augmented neural architectures for learning both transcript abundance and splicing isoform trajectories following exercise stimuli. We examine the structural trade-offs inherent in coupling external memory banks with cross-omics attention mechanisms, discuss the infrastructural requirements for training such models on large-scale human exercise cohorts, and evaluate robustness under data heterogeneity, missingness, and population stratification. Governance challenges including privacy-preserving data sharing, fairness across ancestry groups, algorithmic interpretability, and the sustainability of computationally intensive training pipelines are addressed. Integrating the emerging generation of wearable biosensors and genetically encoded ionic-stress reporters further expands the data landscape and demands anticipatory policy frameworks. By situating memory-augmented multi-omics learning within a socio-technical infrastructure perspective, this work delineates a roadmap for deploying predictive models that are not only accurate but also equitable, interpretable, and operationally viable at scale.