Technology and analytical advances have greatly facilitated the rapid progress of data sciences and health (and health-related) sciences. For example, high-throughput technologies (including those for sequencing and mobile/remote devices) are generating ever-increasingly larger volume data, which are multi-modal and high dimensional in genomics, transcriptomics, epigenomics, proteomics, metabolomics, gut metagenomics, single-cell sequencing and spatial sequencing, electronic health/medical records (EHR/EMR), and social and economic sciences. These are all related to human health concerning medicine and public health. Data sciences have produced and continue to produce advanced and evolving analytics in artificial intelligence (AI), machine learning (ML), and deep learning (DL).
Data sciences can effectively and efficiently integrate and extract intrinsic patterns of complex and often non-linear relationships among biological multi-omics and non-omics (societal/social-economic/behavior/educational/-racial/ethnic/gender/sex/environmental) factors. The comprehensive data integration and feature extraction for analysis and pattern recognition are essential and necessary for powerful/precise diagnosis, prediction, and inferences of disease/health causality and mechanisms, and thus is fundamental for the realization of personalized medicine/health and the equality in health/medical care across various human groups. Integrating health sciences and data sciences to harness the greater power of the ever-increasingly large volume and multi-modal data in health research and advancing the capability of data sciences impose great challenges and hold great promise for improving the quality and availability of healthcare to diverse communities.
Therefore, the overarching goal of TIIDHS is to develop a university-wide cross-school center to perform cutting-edge convergence research across disciplines, innovatively integrate data sciences and health-related sciences, and position Tulane competitively for the emerging challenges and opportunities that are significant for research across multiple schools (such as SOM, SSE, SPHTM, Social Sciences).