Michael Mooney

Office: HRC 14D69E
E-mail: mooneymi@ohsu.edu
URL: https://scholar.google.com/citations?user=cUcjJC8AAAAJ  

Biography and Education

Dr. Mooney is an assistant professor in the Division of Bioinformatics & Computational Biology at OHSU. His research is focused on the development of statistical and computational methods to better understand how DNA variation influences human disease. He is particularly interested in using machine learning techniques to mine integrated data sets (containing both genomic and clinical/environmental variables) with the goal of developing predictive models of disease risk and outcome.

Before joining the department, Michael worked as a staff research associate at UCLA studying the genetics of polycystic kidney disease, and later as an assistant editor at MAMM, a health magazine for breast cancer survivors.

Oregon Health & Science University
Ph.D. in Biomedical Informatics, 2011

New York University
M.A. in Journalism, 2005
Certificate in Science and Environmental Reporting, 2005

University of California, Los Angeles
B.S. in Cybernetics, 2001


Publications and Presentations

Mooney MA, McWeeney SK, Faraone SV, Hinney A, Hebebrand J;IMAGE2 Consortium.;German ADHD GWAS Group., Nigg JT, Wilmot B. Pathway analysis in attention deficit hyperactivity disorder: An ensemble approach. Am J Med Genet B Neuropsychiatr Genet. 2016 Sep;171(6):815-26. doi: 10.1002/ajmg.b.32446.

Mooney MA, Wilmot B. Gene set analysis: A step-by-step guide. Am J Med Genet B Neuropsychiatr Genet. 2015 Oct;168(7):517-27. doi: 10.1002/ajmg.b.32328. Epub 2015 Jun 8.

Eriksson J, Evans DS, Nielson CM, Shen J, Srikanth P, Hochberg M, McWeeney S, Cawthon PM, Wilmot B, Zmuda J, Tranah G, Mirel DB, Challa S, Mooney M, Crenshaw A, Karlsson M, Mellström D, Vandenput L, Orwoll E, Ohlsson C. Limited clinical utility of a genetic risk score for the prediction of fracture risk in elderly subjects. J Bone Miner Res. 2015 Jan;30(1):184-94. doi: 10.1002/jbmr.2314.

Mooney MA, Nigg JT, McWeeney SK, Wilmot B. Functional and genomic context in pathway analysis of GWAS data. Trends Genet. 2014 Sep;30(9):390-400. doi: 10.1016/j.tig.2014.07.004.

Hitzemann R, Bottomly D, Iancu O, Buck K, Wilmot B, Mooney M, Searles R, Zheng C, Belknap J, Crabbe J, McWeeney S. The genetics of gene expression in complex mouse crosses as a tool to study the molecular underpinnings of behavior traits. Mamm Genome. 2014 Feb;25(1-2):12-22. doi: 10.1007/s00335-013-9495-6.

-Mooney M, McWeeney S. Data integration and reproducibility for high-throughput transcriptomics. Int Rev Neurobiol. 2014;116:55-71. doi: 10.1016/B978-0-12-801105-8.00003-5.

Mooney MA, Wilmot B, The Bipolar Genome Study, McWeeney S. The GA and the GWAS: Using Genetic Algorithms to Search for Multi-locus Associations. IEEE/ACM Transactions on Computational Biology and Bioinformatics, May-June 2012, vol. 9, no. 3, pp. 899-910.

Laderas T, Walter N, Mooney M, Vartanian K, Darakjian P, Buck K, Harrington C, Belknap J, Hitzemann R, McWeeney S. Computational Detection of Alternative Exon Usage. Frontiers in Neurogenomics, 2011, 5:69.

Bottomly D, Walter NA, Hunter JE, Darakjian P, Kawane S, Buck KJ, Searles RP, Mooney M, McWeeney SK, Hitzemann R. Evaluating gene expression in C57BL/6J and DBA/2J mouse striatum using RNA-Seq and microarrays. PLOS One, 2011 Mar 24, 6(3):e17820.

Walter NA, Bottomly D, Laderas T, Mooney MA, Darakjian P, Searles RP, Harrington CA, McWeeney SK, Hitzemann R, Buck KJ. High throughput sequencing in mice: a platform comparison identifies a preponderance of cryptic SNPs. BMC Genomics, 2009 Aug 17, 10:379.