ABSTRACT Please see the Volume 1. Technical Proposal. 1. Background and Purpose The Women’s Health Research Initiative (WHRI) recognizes that women have historically been underrepresented in biomedical research, and as a result, there is a lack of understanding of sex and gender-related differences in risk factors and health outcomes. The Initiative rightly emphasizes a need for the application of novel analytical approaches to rich datasets to elucidate these differences, improve our understanding of Women’s Health issues, and inform more effective medical interventions. Initiated in 2014, the Trans-Omics for Precision Medicine (TOPMed) Program is part of the National Heart, Lung, and Blood Institute’s (NHLBI’s) broader precision medicine initiative which aims to improve the prevention and treatment of heart, lung, blood, and sleep (HLBS) disorders through tailored and individualized disease treatments. The TOPMed program generates whole genome sequencing (WGS) and other –omics data and integrates them with molecular, behavioral, imaging, environmental, and clinical data to better understand the biological processes that underlie HLBS disorders. Petabytes of –omics, phenotypic and environmental exposure data generated by TOPMed from approximately 200,000 research subjects across 90+ studies are fed into NHLBI’s BioData Catalyst (BDC) ecosystem and the Database for Genotypes and Phenotypes (dbGaP) for use by scientific investigators. Data collected as part of the TOPMed Program have significant potential to improve women’s health outcomes, however, due to the complexity and multi-factorial nature of the data, novel methods are needed to discover and measure –omics-level sex differences. Recent advances in Artificial Intelligence (AI) and Machine Learning (ML) methods have shown significant promise to uncover previously unknown statistical associations across a range of therapeutic areas, particularly when combining different types of data, such as whole genome sequences, clinical measures, and medical imaging. By bringing together TOPMed data, experienced AI and ML researchers, and subject matter experts in Women’s Health, with the analytical infrastructure provided by the BDC, we will be able to address the long-standing sex-related disparities in biomedical research. The NHLBI is establishing the Artificial Intelligence – Coordinating Center (AI-CC) to serve as a central hub for coordinating research projects and bringing together AI and scientific experts to collaborate on innovative approaches to analyze and interpret TOPMed data. Additionally, the AI-CC may support other areas of NHLBI’s research interest such as the use of AI on imaging and –omics data in the areas of radiomics and radiogenomics to advance the understanding of chronic lung disease, especially idiopathic pulmonary fibrosis. Westat is well-positioned to implement and operate the AI-CC due to the significant efficiencies that will be gained from our role as the TOPMed Administrative Coordinating Center (ACC). Our experience and capabilities include: (1) a strong team of staff that routinely collaborates with NHLBI, TOPMed investigators, and the BDC team; (2) established and effective processes, procedures, and systems that can easily and quickly be adapted and applied to the AI-CC; (3) familiarity with TOPMed data and associated analyses; (4) substantial experience and established processes for managing sub-Other Transaction Authority (OTA) awards; and (5) experience in AI and ML methods. Where feasible, to maximize synergy and to gain most efficiencies, we have selected the staff that currently support TOPMed, augmented with other relevant experts. Our approach will not only leverage the efficiencies afforded by the TOPMed program, but also provide an efficient and scalable mechanism to support additional research areas, beginning with a focus on lung disease.