planet.donarmstrong.com

 A 12-minute overview of my artificial intelligence and machine learning research program [YouTube]

Moore JH. Ten important roles for academic leaders in data science. BioData Min. 2020 Oct 26;13:18. doi: 10.1186/s13040-020-00228-5. PMID: 33117434; PMCID: PMC7586691. [PubMed] [BioData Mining]

Abstract

Data science has emerged as an important discipline in the era of big data and biological and biomedical data mining. As such, we have seen a rapid increase in the number of data science departments, research centers, and schools. We review here ten important leadership roles for a successful academic data science chair, director, or dean. These roles include the visionary, executive, cheerleader, manager, enforcer, subordinate, educator, entrepreneur, mentor, and communicator. Examples specific to leadership in data science are given for each role.

Li R, Chen Y, Ritchie MD, Moore JH. Electronic health records and polygenic risk scores for predicting disease risk. Nat Rev Genet. 2020 Aug;21(8):493-502. doi: 10.1038/s41576-020-0224-1. Epub 2020 Mar 31. PMID: 32235907. [PubMed] [Nature Reviews]

Abstract

Accurate prediction of disease risk based on the genetic make-up of an individual is essential for effective prevention and personalized treatment. Nevertheless, to date, individual genetic variants from genome-wide association studies have achieved only moderate prediction of disease risk. The aggregation of genetic variants under a polygenic model shows promising improvements in prediction accuracies. Increasingly, electronic health records (EHRs) are being linked to patient genetic data in biobanks, which provides new opportunities for developing and applying polygenic risk scores in the clinic, to systematically examine and evaluate patient susceptibilities to disease. However, the heterogeneous nature of EHR data brings forth many practical challenges along every step of designing and implementing risk prediction strategies. In this Review, we present the unique considerations for using genotype and phenotype data from biobank-linked EHRs for polygenic risk prediction.

Moore JH, Olson RS, Schmitt P, Chen Y, Manduchi E. How Computational Experiments Can Improve Our Understanding of the Genetic Architecture of Common Human Diseases. Artif Life. 2020 Winter;26(1):23-37. doi: 10.1162/artl_a_00308. Epub 2020 Feb 6. PMID: 32027528. [PubMed] [Artificial Life]

Abstract