A natural language processing-driven map of the aging research landscape

Area of Science:

• Gerontology and Biomedical Informatics
• Computational Biology
• Translational Medicine

Background:

• Aging research has expanded exponentially, necessitating advanced synthesis tools beyond traditional reviews.
• The complexity of aging literature requires unbiased, data-driven approaches for comprehensive analysis.
• Understanding the evolution and thematic landscape of aging research is crucial for future directions.

Purpose of the Study:

• To analyze the thematic evolution of aging research over a century using advanced NLP and ML techniques.
• To identify key research clusters, shifting priorities, and translational gaps in the field.
• To provide a scalable, data-driven alternative to conventional narrative reviews in aging research.

Main Methods:

• Analysis of 461,789 aging research abstracts published between 1925 and 2023.
• Integration of Latent Dirichlet Allocation (LDA), TF-IDF, dimensionality reduction, and clustering algorithms.
• Utilized natural language processing (NLP) and machine learning (ML) for thematic landscape delineation.

Main Results:

• A significant shift in aging research focus from early cellular/molecular mechanisms to recent clinical studies, especially neurodegenerative disorders.
• Identification of a persistent conceptual divide between the biology of aging (BoA) and clinical research domains.
• Discovery of distinct research clusters, including potentially overlooked biological processes, and mapping of interconnections.

Conclusions:

• Aging research priorities have shifted towards clinical applications, highlighting translational gaps.
• NLP and ML offer a powerful, scalable method for synthesizing vast scientific literature.
• The identified thematic landscape and interconnections can guide future research directions in gerontology.