一种用于从全基因组数据预测临床结果的高效贝叶斯方法。
An efficient bayesian method for predicting clinical outcomes from genome-wide data.
作者信息
Cooper Gregory F, Hennings-Yeomans Pablo, Visweswaran Shyam, Barmada Michael
机构信息
Department of Biomedical Informatics.
出版信息
AMIA Annu Symp Proc. 2010 Nov 13;2010:127-31.
Abstract
This paper compares the predictive performance and efficiency of several machine-learning methods when applied to a genome-wide dataset on Alzheimer's disease that contains 312,318 SNP measurements on 1411 cases. In particular, a Bayesian algorithm is introduced and compared to several standard machine-learning methods. The results show that the Bayesian algorithm predicts outcomes comparably to the standard methods, and it requires less total training time. These results support the further development and evaluation of the Bayesian algorithm.
摘要
本文比较了几种机器学习方法应用于一个关于阿尔茨海默病的全基因组数据集时的预测性能和效率，该数据集包含1411例病例的312318个单核苷酸多态性（SNP）测量值。特别地，引入了一种贝叶斯算法并将其与几种标准机器学习方法进行比较。结果表明，贝叶斯算法在预测结果方面与标准方法相当，并且所需的总训练时间更少。这些结果支持对贝叶斯算法进行进一步开发和评估。
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