高维微阵列数据线性回归的贝叶斯变量选择

Wellington Cabrera, C. Ordonez, D. S. Matusevich, V. Baladandayuthapani
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引用次数: 4

摘要

变量选择是贝叶斯统计中的一个基本问题,它的解决需要探索一个组合搜索空间。我们用一种著名的MCMC方法研究了变量选择的解,该方法需要数千次迭代。我们提出了几个算法优化来加速MCMC方法,使其在数据库系统中有效地工作。我们的优化包括充分的统计、变量预选、哈希表和调用线性代数库。我们提出了用高维微阵列数据集预测癌症生存时间的实验。我们讨论了令人鼓舞的发现,确定了可能预测脑癌患者生存时间的特定基因。我们还展示了基于dbms的算法比R统计包快几个数量级。我们的工作表明,DBMS是一个很有前途的平台来分析微阵列数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bayesian variable selection for linear regression in high dimensional microarray data
Variable selection is a fundamental problem in Bayesian statistics whose solution requires exploring a combinatorial search space. We study the solution of variable selection with a well-known MCMC method, which requires thousands of iterations. We present several algorithmic optimizations to accelerate the MCMC method to make it work efficiently inside a database system. Our optimizations include sufficient statistics, variable preselection, hash tables and calling a linear algebra library. We present experiments with very high dimensional microarray data sets to predict cancer survival time. We discuss encouraging findings, identifying specific genes likely to predict the survival time for brain cancer patients. We also show our DBMS-based algorithm is orders of magnitude faster than the R statistical package. Our work shows a DBMS is a promising platform to analyze microarray data.
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