Statistical models for genetics data are often surprisingly challenging, and often require advanced and new statistical methods. Using probability machines on whole genome data is a recent invention, with the original research on probability machines appearing in Methods of Information in Medicine (September 2011). Our methods point to refined and personalized probability predictions using a wide range of biomarkers, medical information and whole genome data. The detection of childhood-onset schizophrenia using 800,000 snps using probability machines has error rates of 15% or less, and the list of predictive snps can be filtered down to a list of less than a few hundred. Other studies of psychiatric conditions (ADHD, bipolar) are also now underway using probability machines and personalized medicine, subject-specific predictions.

Project Start
Project End
Budget Start
Budget End
Support Year
14
Fiscal Year
2012
Total Cost
$11,052
Indirect Cost
Name
Center for Information Technology
Department
Type
DUNS #
City
State
Country
Zip Code
Shah, Mona; Mamyrova, Gulnara; Targoff, Ira N et al. (2013) The clinical phenotypes of the juvenile idiopathic inflammatory myopathies. Medicine (Baltimore) 92:25-41
Kim, Yoonhee; Li, Qing; Cropp, Cheryl D et al. (2011) Performance of random forests and logic regression methods using mini-exome sequence data. BMC Proc 5 Suppl 9:S104
Dasgupta, Abhijit; Sun, Yan V; König, Inke R et al. (2011) Brief review of regression-based and machine learning methods in genetic epidemiology: the Genetic Analysis Workshop 17 experience. Genet Epidemiol 35 Suppl 1:S5-11