We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Performance of random forests and logic regression methods using mini-exome sequence data.
- Authors
Yoonhee Kim; Qing Li; Cropp, Cheryl D.; Heejong Sung; Juanliang Cai; Simpson, Claire L.; Perry, Brian; Dasgupta, Abhijit; Malley, James D.; Wilson, Alexander F.; Bailey-Wilson, Joan E.
- Abstract
Machine learning approaches are an attractive option for analyzing large-scale data to detect genetic variants that contribute to variation of a quantitative trait, without requiring specific distributional assumptions. We evaluate two machine learning methods, random forests and logic regression, and compare them to standard simple univariate linear regression, using the Genetic Analysis Workshop 17 mini-exome data. We also apply these methods after collapsing multiple rare variants within genes and within gene pathways. Linear regression and the random forest method performed better when rare variants were collapsed based on genes or gene pathways than when each variant was analyzed separately. Logic regression performed better when rare variants were collapsed based on genes rather than on pathways.
- Subjects
GENES; HUMAN genetic variation; LOGISTIC regression analysis; HEREDITY; MOLECULAR genetics
- Publication
BMC Proceedings, 2011, Vol 5, Issue Suppl 9, p1
- ISSN
1753-6561
- Publication type
Article
- DOI
10.1186/1753-6561-5-S9-S104