Resource Core 3 (RC3), Data Science

Cedars-Sinai Medical Center, West Hollywood CA

Investigators

Abstract

PROJECT SUMMARY – RESOURCE CORE 3 (RC3), DATA SCIENCE CORE A major goal of geroscience research is to discover therapies or interventions that can extend healthy lifespan, or healthspan, by modulating the biology of aging. The emergent field of translational geroscience, which seeks to discover and test gerotherapeutic interventions that can delay or prevent age-related diseases and their deleterious effects, poses many potential analytical challenges. First, studies focused on aging biology use varied types of data to measure aspects of healthspan, spanning from cellular and molecular data to clinical phenotype data. Second, this research requires discovery of biomarkers of aging that can enable quantification of therapeutic effects. Translational geroscience uses data ranging from genomic sequences to proteins and metabolites to phenotypic screens and clinical trials. To overcome analytical challenges, novel bioinformatics, biostatistical, and artificial intelligence (AI) methods are needed to integrate and manage data, predict aging trajectories and responses, and assist with drug discovery. However, researchers must navigate issues related to the accessibility and usability of data science technologies, along with the interpretability and explainability of results to biologists and clinicians. These can present important barriers to widespread adoption of these technologies by geroscience researchers and clinicians. In alignment with the overarching goal of the Los Angeles Older Americans Independence Center (LA OAIC), we propose a Resource Core 3 (RC3) Data Science Core that will provide data science expertise, software, and training for basic aging and clinical translational research projects focused on advancing translational geroscience to extend human healthspan. Specifically, the core will develop and make available a state-of-the-art database for storing, integrating, and managing multimodal geroscience data (Aim 1). Further, we will offer biostatistical and bioinformatics consultations for experimental design, quality control, and data analysis (Aim 2). In Aim 3, we will make available computational expertise and user-friendly software for AI analysis of data. In Aim 4, we will lead two Developmental Projects using novel computational methods. Finally, in Aim 5, we will provide workshops and training opportunities on data management, data analysis, and AI methods for geroscience researchers. These aims will provide an important foundation for advancing geroscience research by assisting investigators with data integration, management, and analysis in the AI era.