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Jason H. Moore

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Jason H. Moore
NationalityUSA
Alma materFlorida State University
University of Michigan
Known forMultifactor dimensionality reduction
AwardsFellow of AAAS, ACMI, ASA
Scientific career
FieldsTranslational Bioinformatics
Biomedical Informatics
Human Genetics
InstitutionsVanderbilt University
Dartmouth College
University of Pennsylvania
Academic advisorsCharles F. Sing, Ph.D.
Notable studentsMarylyn D. Ritchie

Jason H. Moore is a translational bioinformatics scientist, biomedical informatician, and human geneticist, the Edward Rose Professor of Informatics and Director of the Institute for Biomedical Informatics at the Perelman School of Medicine at the University of Pennsylvania, where he is also Senior Associate Dean for Informatics and Director of the Division of Informatics in the Department of Biostatistics, Epidemiology, and Informatics.

Biography

He was a founding Director of the Advanced Computing Center for Research and Education at Vanderbilt University from 2000 until 2004 and founding Director of the Institute for Quantitative Biomedical Sciences at Geisel School of Medicine of Dartmouth College from 2010 until 2015.

He's the editor-in-chief of the BioData Mining journal since 2008.

Research

Moore's research focuses on the development and application of artificial intelligence and machine learning methods for modeling complex patterns in biomedical big data. A central focus is using informatics methods for identifying combinations of DNA sequence variations and environmental factors that are predictive of human health and complex disease. For example, he developed the multifactor dimensionality reduction (MDR)[1][2] machine learning method for detecting and characterizing combinations of attributes or independent variables that interact to influence a dependent or class variable. He then applied MDR for improved understanding of the interplay of multiple genetic polymorphisms of complex traits in genome-wide association studies. More recent work focuses on computational methods such as the tree-based pipeline optimization tool (TPOT)[3][4] for automated machine learning and data science. Current work also focuses on methods and software for accessible artificial intelligence.[5][6]

He is a former member of the National Library of Medicine grant review committee (BLIRC). He is the founding Editor-in-Chief of the journal BioData Mining. He has published more than 500 peer reviewed articles, book chapters and editorials. His translational bioinformatics research program has been continuously funded by multiple grants from the National Institutes of Health for nearly 20 years.

Honors

In 2011 he was elected as a Fellow of the American Association for the Advancement of Science (AAAS)[7] and was selected as a Kavli Fellow of the National Academy of Sciences (NAS)[8] in 2013. In 2015 he was elected a Fellow of the American College of Medical Informatics (ACMI).[9] In 2017 he was elected a Fellow of the American Statistical Association (ASA).[10]

References

  1. ^ Ritchie, Marylyn D.; Hahn, Lance W.; Roodi, Nady; Bailey, L. Renee; Dupont, William D.; Parl, Fritz F.; Moore, Jason H. (2001-07-01). "Multifactor-Dimensionality Reduction Reveals High-Order Interactions among Estrogen-Metabolism Genes in Sporadic Breast Cancer". The American Journal of Human Genetics. 69 (1): 138–147. doi:10.1086/321276. PMC 1226028. PMID 11404819.
  2. ^ Hahn, L. W.; Ritchie, M. D.; Moore, J. H. (2003-02-12). "Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions". Bioinformatics. 19 (3): 376–382. doi:10.1093/bioinformatics/btf869. ISSN 1367-4803.
  3. ^ Olson, Randal S.; Urbanowicz, Ryan J.; Andrews, Peter C.; Lavender, Nicole A.; Kidd, La Creis; Moore, Jason H. (2016-03-30). Automating Biomedical Data Science Through Tree-Based Pipeline Optimization. Lecture Notes in Computer Science. Vol. 9597. Springer, Cham. pp. 123–137. arXiv:1601.07925. doi:10.1007/978-3-319-31204-0_9. ISBN 978-3-319-31203-3. {{cite book}}: |journal= ignored (help)
  4. ^ Olson, Randal S.; Bartley, Nathan; Urbanowicz, Ryan J.; Moore, Jason H. (2016-01-01). Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science. GECCO '16. New York, NY, USA: ACM. pp. 485–492. arXiv:1603.06212. doi:10.1145/2908812.2908918. ISBN 9781450342063. {{cite book}}: |journal= ignored (help)
  5. ^ Olson, Randal S.; Sipper, Moshe; La Cava, William; Tartarone, Sharon; Vitale, Steven; Fu, Weixuan; Holmes, John H.; Moore, Jason H. (2017-05-01). "A System for Accessible Artificial Intelligence". arXiv:1705.00594 [cs.AI].
  6. ^ "These Researchers Want the People to Seize the Means of AI Production". Motherboard. 2017-05-03. Retrieved 2017-05-06.
  7. ^ "AAAS Members Elected as Fellows". AAAS - The World's Largest General Scientific Society. 2011-12-06. Retrieved 2017-05-06.
  8. ^ "Geisel School of Medicine - Dartmouth's Jason Moore selected as a Kavli Fellow of the National Academy of Sciences". geiselmed.dartmouth.edu. Retrieved 2017-05-06.
  9. ^ "13 Fellows Inducted into American College of Medical Informatics". PRWeb. Retrieved 2017-05-06.
  10. ^ "Director of the Penn Institute for Biomedical Informatics Jason Moore Elected as a Fellow of the American Statistical Association – PR News". www.pennmedicine.org. Retrieved 2017-05-06.

External links

Retrieved from "https://en.wikipedia.org/w/index.php?title=Jason_H._Moore&oldid=950354201"