Data science tools for accelerating alcohol research

Only Phase I proposals will be accepted. Fast-Track proposals will not be accepted. Number of anticipated awards: 1-2 Budget (total costs, per award) Phase I: up to $250,000 for 6-12 months. Summary NIAAA supported studies in genomics, imaging, electrophysiology and optogenetics, electronic health records, and personal wearable devices present challenges in analyses and interpretations and opportunities for discovery. The Final NIH Data Management and Sharing Policy (NOT-OD-21-013) requires data sharing for research project applications submitted after January 25, 2023. In addition, NIAAA expects that investigators and their institutions to submit their scientific data from human subject to an NIAAA-sponsored data repository, namely the NIAAA Data Archive (NOT-AA-23-002). Datasets in the NIAAA Data Archive may be combined with current data repositories and archives, including: database of Genotypes and Phenotype,(dbGaP), Collaborative Studies on Genetics of Alcoholism (COGA), National Epidemiologic Survey on Alcohol and Related Conditions-III (NESARC-III), All of Us, Adolescent Brain Cognitive Development (ABCD) study, and the HEALthy Brain and Child Development Study (HBCD), to form a large and rich source for analysis of alcohol use, suitable for analysis by data science methods. Examples of electronic health records (EHRs) include: (NIH-funded) Health Care Systems Research Collaboratory, Clinical and Translational Science Awards (CTSA) Program, and the Health Care Systems Research Network. Data science includes and extends beyond bioinformatics and computational neuroscience to discover new relationships and pathways for complex systems of normal human function and during adaptations due to disorders or disease. However, many of the tools needed to answer questions in alcohol research require specific applications, algorithms or toolkits that are not currently available. User-friendly methods and applications program interfaces (APIs) for retrieving metadata and data from the repositories for secondary analyses are not currently available. Alcohol researchers require assistance from data scientists to appreciate the power of tools such as machine learning, deep learning and artificial intelligence and the skills for programmers to implement analysis methods to answer key questions about alcohol use. NIAAA is interested in analytical approaches and tools that can integrate data (i.e. genetic, social, economic, EHR, treatment approaches) to predict the development of alcohol use disorder, or the effectiveness of interventions that reduce or delay the onset or progression of alcohol use disorder, or guide effective treatment and management strategies for alcohol use disorder, including recovery and relapse. One possible example is a tool that assists researchers in developing a risk algorithm for alcohol use disorder when researchers combine multiple existing data sets. The tool could parse out age, sex, quantity/frequency/binge alcohol use, consequences and other risk factors.