Key result
Penalized partial least squares outperforms standard methods in predicting LVAD support time from gene expression.
Observational
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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.
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Huang et al. (2004) conducted an observational in Heart failure. Penalized partial least squares (PPLS) regression model for LVAD support duration vs. Partial least squares and Breiman's random forest method was evaluated on Prediction of the relationship between LVAD support time and gene expression changes. Penalized partial least squares (PPLS) provided better prediction of LVAD support time from myocardial gene expression changes than standard partial least squares or random forest methods.
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