Huang Xiaohong, Pan Wei, Park Soon, Han Xinqiang, Miller Leslie W, Hall Jennifer
Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, MMC 303, Minneapolis, MN 55455-0378, USA.
Bioinformatics. 2004 Apr 12;20(6):888-94. doi: 10.1093/bioinformatics/btg499. Epub 2004 Jan 29.
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.
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.
心力衰竭影响着全球超过2000万人。心脏移植是最有效的治疗方法，但符合条件的患者数量远远超过可用供体心脏的数量。左心室机械辅助装置（LVAD）已被开发为一种成功的替代疗法，在患者等待供体心脏时辅助衰竭的心室。我们从LVAD植入和取出时采集的配对人类心脏样本中获得了基因组学数据。我们研究中的心力衰竭患者接受LVAD支持的时间各不相同。本研究的目的是建立LVAD支持时间与基因表达变化之间的关系模型。
为实现这一目的，我们提出了一种新的惩罚偏最小二乘法（PPLS）来建立回归模型。与偏最小二乘法和布莱曼随机森林法相比，PPLS对LVAD数据给出了最佳预测结果。
