Modeling the relationship between LVAD support time and gene expression changes in the human heart by penalized partial least squares

Authors:
Xiaohong Huang;Wei Pan;Soon Park;Xinqiang Han;Leslie W. Miller;Jennifer Hall
Affiliations:
Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, MMC 303, Minneapolis, MN 55455-0378, USA,;Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, MMC 303, Minneapolis, MN 55455-0378, USA,;Cardiothoracic Surgery, Fairview Medical Center, University of Minnesota, Minneapolis, MN 55455, USA;Department of Medicine, Cardiovascular Division, Medical School, University of Minnesota, Minneapolis, MN 55455, USA;Department of Medicine, Cardiovascular Division, Medical School, University of Minnesota, Minneapolis, MN 55455, USA;Department of Medicine, Cardiovascular Division, Medical School, University of Minnesota, Minneapolis, MN 55455, USA
Venue:
Bioinformatics
Year:
2004

Citing 0
Cited 2

Temporal gene expression classification with regularised neural network

International Journal of Bioinformatics Research and Applications
Borrowing information from relevant microarray studies for sample classification using weighted partial least squares

Computational Biology and Chemistry

Quantified Score

Hi-index	3.84

Visualization

Abstract

Motivation: Heart failure affects more than 20 million people in the world. Heart transplantation is the most effective therapy, but the number of eligible patients far outweighs the number of available donor hearts. The left mechanical ventricular assist device (LVAD) has been developed as a successful substitution therapy that aids the failing ventricle while a patient is waiting for the donor heart. We obtained genomics data from paired human heart samples harvested at the time of LVAD implant and explant. The heart failure patients in our study were supported by the LVAD for various periods of time. The goal of this study is to model the relationship between the time of LVAD support and gene expression changes. Results: To serve the purpose, we propose a novel penalized partial least squares (PPLS) method to build a regression model. Compared with partial least squares and Breiman's random forest method, PPLS gives the best prediction results for the LVAD data.