A comparative study of discriminating human heart failure etiology using gene expression profiles

Background: Human heart failure is a complex disease that manifests from multiple genetic and environmental factors. Although ischemic and non-ischemic heart disease present clinically with many similar decreases in ventricular function, emerging work suggests that they are distinct diseases with different responses to therapy. The ability to distinguish between ischemic and nonischemic heart failure may be essential to guide appropriate therapy and determine prognosis for successful treatment. In this paper we consider discriminating the etiologies of heart failure using gene expression libraries from two separate institutions. Results: We apply five new statistical methods, including partial least squares, penalized partial least squares, LASSO, nearest shrunken centroids and random forest, to two real datasets and compare their performance for multiclass classification. It is found that the five statistical methods perform similarly on each of the two datasets: it is difficult to c...

• Type: Journal
• Year: 2005
• Where: BMCBI
• Authors: Xiaohong Huang, Wei Pan, Suzanne Grindle, Xinqiang Han, Yingjie Chen, Soon J. Park, Leslie W. Miller, Jennifer Hall
• Keywords: BMCBI 2005, Gene Expression, Heart Failure, Human Heart Failure