LASSO-Patternsearch algorithm with application to ophthalmology and genomic data.
LASSO-Patternsearch algorithm with application to ophthalmology and genomic data.
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DOI：
10.4310/sii.2008.v1.n1.a12
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发表时间：
2006-10
影响因子：
0.8
通讯作者：
Weiliang Shi;G. Wahba;S. Wright;Kristine E. Lee;R. Klein;B. Klein
中科院分区：
文献类型：
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作者：
Weiliang Shi;G. Wahba;S. Wright;Kristine E. Lee;R. Klein;B. Klein
LASSO-Patternsearch算法用于有效识别人口统计学和基因组学研究中多个二分类风险因素的模式。所考虑的模式是由多元伯努利密度的对数线性展开自然产生的模式。该方法是为可能存在大量候选模式，但据信只有相对较少的数量是重要的情况而设计的。LASSO使用了一种新颖的计算算法，可以同时处理大量的未知数，从而大大减少了候选模式的数量。幸存的模式在（参数）广义线性模型的框架中被进一步修剪。在这两个步骤中都使用了一种基于伯努利结果的GACV的新型调谐过程，该过程被修改为模型选择器。我们将该方法应用于基于人群的海狸坝眼研究的近视数据，揭示生理学上有趣的相互作用的危险因素。然后，我们将该方法应用于基于遗传分析研讨会15的问题3的类风湿性关节炎生成模型的数据，成功地展示了其从基因组研究的典型长度属性向量中有效恢复高阶模式的潜力。
The LASSO-Patternsearch algorithm is proposed to efficiently identify patterns of multiple dichotomous risk factors for outcomes of interest in demographic and genomic studies. The patterns considered are those that arise naturally from the log linear expansion of the multivariate Bernoulli density. The method is designed for the case where there is a possibly very large number of candidate patterns but it is believed that only a relatively small number are important. A LASSO is used to greatly reduce the number of candidate patterns, using a novel computational algorithm that can handle an extremely large number of unknowns simultaneously. The patterns surviving the LASSO are further pruned in the framework of (parametric) generalized linear models. A novel tuning procedure based on the GACV for Bernoulli outcomes, modified to act as a model selector, is used at both steps. We applied the method to myopia data from the population-based Beaver Dam Eye Study, exposing physiologically interesting interacting risk factors. We then applied the the method to data from a generative model of Rheumatoid Arthritis based on Problem 3 from the Genetic Analysis Workshop 15, successfully demonstrating its potential to efficiently recover higher order patterns from attribute vectors of length typical of genomic studies.