Digital Resources for Traversing the Landscape of Human Aging: Databases, Key Computational Strategies, and Artificial Intelligence.

Abstract

By 2050, nearly 20% of the global population will exceed 60 years old, experiencing compromised physiological and functional abilities, neurological disorders, and sarcopenia. Geroscience has evolved immensely through OMICS approaches and high-throughput technologies, generating massive datasets requiring efficient management, annotation, and storage. This highlights the need for user-friendly databases integrated with machine learning (ML) and artificial intelligence (AI). This review provides a comparative synthesis of the state-of-the-art databases on longevity genes, age-related signaling pathways, model organism phenotypes, and manually curated aging/antiaging experimental studies. We delineate the architecture, methodology, and functional features of contemporary geroscience databases, detailing the objectives, content, and dataset size, including multiomics information. Critically, these databases facilitate geriatric interventions: from biomarker discovery to drug repositioning, significantly impacting aging-associated conditions like muscle loss and Alzheimer's. In addition, we present updated insights into the increasing use of deep aging clocks and multiomics databases, coupled with ML- and AI-dependent analyses, fostering advanced dataset development. Interestingly, these tools are capable of dataset pattern recognition, predictive modeling, and the generation of research hypotheses. By bridging manually curated and AI-driven tools, this review offers a holistic view of the complementary strengths of aging databases, paving the way for the next generation of geroscience.