JSM 2012 Home

JSM 2012 Online Program

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

Online Program Home

Abstract Details

Activity Number: 253
Type: Contributed
Date/Time: Monday, July 30, 2012 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #305297
Title: Predicting the Lethal Phenotype of the Knockout Mouse by Integrating Comprehensive Genomic Data
Author(s): Robyn Ball*+ and Yuan Yuan and Yanxun Xu and Jianfeng Xu and Han Liang
Companies: and Baylor College of Medicine and Rice University/MD Anderson Cancer Center and Alliance Data and MD Anderson Cancer Center
Address: 904 University Oaks, College Station, TX, 77840, United States
Keywords: random forest ; machine learning ; knockout mouse

The mouse is widely used as a model organism. A conventional approach in mouse genetics research is to investigate the phenotypic consequence of knocking out a gene. One commonly-studied phenotype is lethality, a binary outcome. Using data collected from the Mouse Genome Informatics (MGI) Database, we compared the accuracy of three genomic classifiers for predicting lethality. Each of logistic regression, support vector machine, and random forest was used for classification, with feature-selection carried out by LASSO in each case. Error rates were estimated with cross validation. We found that the random forest classifier almost always outperformed the other methods, with an estimated accuracy of ~74%.

The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.

Back to the full JSM 2012 program

2012 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Continuing Education program, please contact the Education Department.