Simulation studies as designed experiments: the comparison of penalized regression models in the "large p, small n" setting.
Simulation studies as designed experiments: the comparison of penalized regression models in the "large p, small n" setting.
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DOI：
10.1371/journal.pone.0107957
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Margolin AA
中科院分区：
文献类型：
--
作者：
Chaibub Neto E;Bare JC;Margolin AA
在计算生物学中，新的算法不断被提出。新方法的性能评估在实践中很重要。尽管如此，实地经验表明，缺乏旨在系统和客观地评价相互竞争的方法的严格方法。仿真研究经常被用来表明一种特定的方法优于另一种方法。然而，很多时候，模拟研究并没有很好的设计，很难描述不同方法表现更好的特定条件。在本文中，我们提出了通过完善的技术在计算机和物理实验的设计开发有效的模拟研究。通过遵循实验规划中的最佳实践，我们能够更好地了解竞争算法的优势和劣势，从而更明智地决定使用哪种方法来执行特定任务。我们说明了我们提出的模拟框架的应用程序与详细比较的脊回归，套索和弹性网络算法在一个大规模的研究调查预测性能的影响样本量，特征数量，真实模型稀疏性，信噪比和特征相关性，在协变量的数量通常是远远大于样本量的情况下。包含数万个特征但只有几百个样本的数据集的分析如今在计算生物学中是常规的，其中“组学”特征如基因表达、拷贝数变异和序列数据经常用于复杂表型如抗癌药物反应的预测建模。本研究中研究的惩罚回归方法是这种情况下的流行选择，我们的模拟证实了关于这些方法中的每一种方法预期表现最佳的条件的既定结果，同时提供了一些新的见解。
New algorithms are continuously proposed in computational biology. Performance evaluation of novel methods is important in practice. Nonetheless, the field experiences a lack of rigorous methodology aimed to systematically and objectively evaluate competing approaches. Simulation studies are frequently used to show that a particular method outperforms another. Often times, however, simulation studies are not well designed, and it is hard to characterize the particular conditions under which different methods perform better. In this paper we propose the adoption of well established techniques in the design of computer and physical experiments for developing effective simulation studies. By following best practices in planning of experiments we are better able to understand the strengths and weaknesses of competing algorithms leading to more informed decisions about which method to use for a particular task. We illustrate the application of our proposed simulation framework with a detailed comparison of the ridge-regression, lasso and elastic-net algorithms in a large scale study investigating the effects on predictive performance of sample size, number of features, true model sparsity, signal-to-noise ratio, and feature correlation, in situations where the number of covariates is usually much larger than sample size. Analysis of data sets containing tens of thousands of features but only a few hundred samples is nowadays routine in computational biology, where “omics” features such as gene expression, copy number variation and sequence data are frequently used in the predictive modeling of complex phenotypes such as anticancer drug response. The penalized regression approaches investigated in this study are popular choices in this setting and our simulations corroborate well established results concerning the conditions under which each one of these methods is expected to perform best while providing several novel insights.
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影响因子：
2.5
作者：
HOERL, AE;KENNARD, RW
通讯作者：
KENNARD, RW
影响因子：
64.8
作者：
Garnett, Mathew J.;Edelman, Elena J.;Heidorn, Sonja J.;Greenman, Chris D.;Dastur, Anahita;Lau, King Wai;Greninger, Patricia;Thompson, I. Richard;Luo, Xi;Soares, Jorge;Liu, Qingsong;Iorio, Francesco;Surdez, Didier;Chen, Li;Milano, Randy J.;Bignell, Graham R.;Tam, Ah T.;Davies, Helen;Stevenson, Jesse A.;Barthorpe, Syd;Lutz, Stephen R.;Kogera, Fiona;Lawrence, Karl;McLaren-Douglas, Anne;Mitropoulos, Xeni;Mironenko, Tatiana;Thi, Helen;Richardson, Laura;Zhou, Wenjun;Jewitt, Frances;Zhang, Tinghu;O'Brien, Patrick;Boisvert, Jessica L.;Price, Stacey;Hur, Wooyoung;Yang, Wanjuan;Deng, Xianming;Butler, Adam;Choi, Hwan Geun;Chang, JaeWon;Baselga, Jose;Stamenkovic, Ivan;Engelman, Jeffrey A.;Sharma, Sreenath V.;Delattre, Olivier;Saez-Rodriguez, Julio;Gray, Nathanael S.;Settleman, Jeffrey;Futreal, P. Andrew;Haber, Daniel A.;Stratton, Michael R.;Ramaswamy, Sridhar;McDermott, Ultan;Benes, Cyril H.
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Benes, Cyril H.
影响因子：
2.2
作者：
Pronzato, Luc;Mueller, Werner G.
通讯作者：
Mueller, Werner G.
影响因子：
64.8
作者：
Barretina, Jordi;Caponigro, Giordano;Stransky, Nicolas;Venkatesan, Kavitha;Margolin, Adam A.;Kim, Sungjoon;Wilson, Christopher J.;Lehar, Joseph;Kryukov, Gregory V.;Sonkin, Dmitriy;Reddy, Anupama;Liu, Manway;Murray, Lauren;Berger, Michael F.;Monahan, John E.;Morais, Paula;Meltzer, Jodi;Korejwa, Adam;Jane-Valbuena, Judit;Mapa, Felipa A.;Thibault, Joseph;Bric-Furlong, Eva;Raman, Pichai;Shipway, Aaron;Engels, Ingo H.;Cheng, Jill;Yu, Guoying K.;Yu, Jianjun;Aspesi, Peter, Jr.;de Silva, Melanie;Jagtap, Kalpana;Jones, Michael D.;Wang, Li;Hatton, Charles;Palescandolo, Emanuele;Gupta, Supriya;Mahan, Scott;Sougnez, Carrie;Onofrio, Robert C.;Liefeld, Ted;MacConaill, Laura;Winckler, Wendy;Reich, Michael;Li, Nanxin;Mesirov, Jill P.;Gabriel, Stacey B.;Getz, Gad;Ardlie, Kristin;Chan, Vivien;Myer, Vic E.;Weber, Barbara L.;Porter, Jeff;Warmuth, Markus;Finan, Peter;Harris, Jennifer L.;Meyerson, Matthew;Golub, Todd R.;Morrissey, Michael P.;Sellers, William R.;Schlegel, Robert;Garraway, Levi A.
通讯作者：
Garraway, Levi A.
DOI：
10.1111/j.1467-9868.2005.00503.x
发表时间：
2005-01-01
影响因子：
5.8
作者：
Zou, H;Hastie, T
通讯作者：
Hastie, T