Moore Research Lab Cedars-Sinai Skip to content Close Select your preferred language English عربى 简体中文 繁體中文 فارسي עִברִית 日本語 한국어 Русский Español Tagalog English English عربى 简体中文 繁體中文 فارسي עִברִית 日本語 한국어 Русский Español Tagalog Translation is unavailable for Internet Explorer Cedars-Sinai Home 1-800-CEDARS-1 1-800-CEDARS-1 Close Find a Doctor Locations Programs & Services Health Library Patient & Visitors Community My CS-Link RESEARCH clear Go Close Navigation Links Academics Faculty Development Community Engagement Calendar Research Research Areas Research Labs Departments & Institutes Find Clinical Trials Research Cores Research Administration Basic Science Research Clinical & Translational Research Center (CTRC) Technology & Innovations News & Breakthroughs Education Graduate Medical Education Continuing Medical Education Graduate School of Biomedical Sciences Professional Training Programs Medical Students Campus Life Office of the Dean Simulation Center Medical Library Program in the History of Medicine About Us All Education Programs Departments & Institutes Faculty Directory Moore Lab The primary goal of the Artificial Intelligence Innovation Laboratory (A2I) is to understand human health as a complex adaptive system so that we can improve the prediction, diagnosis, prevention and treatment of common diseases such as Alzheimer’s, cancer and cardiovascular disease. We take a computational approach focused on the development, evaluation and application of innovative AI, machine learning and systems approaches to modeling biomedical big data for precision health. We are particularly interested in AI methods that are accessible, explainable, fair, open, transparent and unbiased. Recent work has focused on automated machine learning (AutoML) methods which are able to bring this challenging technology to nonexperts. Our tree-based pipeline optimization tool (TPOT) algorithm and software was one of the first AutoML methods. Personal Statement I believe that creativity is the fuel that drives scientific discovery. As such, we value and promote diversity, equity, inclusion, collaboration and transparency in the laboratory. This culture facilitates the creation of cutting-edge new algorithms for solving the most challenging problems in biomedical research and healthcare. Jason H. Moore, PhD Breakthrough Research Areas Our A2I laboratory has been a pioneer in the development, evaluation and application of automated machine-learning methods for biomedical data analysis. For example, our TPOT algorithm and open-source software automatically builds an entire machine-learning pipeline with algorithms for feature selection, feature engineering, feature transformation and machine learning. This takes the guesswork out of machine learning—making this important technology accessible to more users. Resources All our software is released as open-source and available on GitHub. Meet Our Team Our collaborative team includes biologists, computer scientists, data scientists, engineers, mathematicians and statisticians. Publications Scaling tree-based automated machine learning to biomedical big data with a feature set selector. Le TT, Fu W, Moore JH. Bioinformatics. 2020 Jan 1;36(1):250-256. Electronic health records and polygenic risk scores for predicting disease risk. Li R, Chen Y, Ritchie MD, Moore JH. Nat Rev Genet. 2020 Aug;21(8):493-502. Evaluating recommender systems for AI-driven biomedical informatics. La Cava W, Williams H, Fu W, Vitale S, Srivatsan D, Moore JH. Bioinformatics. 2021 Apr 19;37(2):250-256. Lossless integration of multiple electronic health records for identifying pleiotropy using summary statistics. Li R, Duan R, Zhang X, Lumley T, Pendergrass S, Bauer C, Hakonarson H, Carrell DS, Smoller JW, Wei WQ, et al. Nat Commun. 2021 Jan 8;12(1):168. Contact the Moore Lab Pacific Design Center 700 N. San Vicente Blvd., Suite G540 West Hollywood, CA 90069 310‐423‐3521 Send a Message Please ensure Javascript is enabled for purposes of website accessibility