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首页> 外文期刊>Environmental Science & Technology >Building Quantitative Prediction Models for Tissue Residue of Two Explosives Compounds in Earthworms from Microarray Gene Expression Data
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Building Quantitative Prediction Models for Tissue Residue of Two Explosives Compounds in Earthworms from Microarray Gene Expression Data

机译：从微阵列基因表达数据建立Earth中两种炸药组织残留的定量预测模型

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摘要

Soil contamination near munitions plants and testing grounds is a serious environmental concern that can result in the formation of tissue chemical residue in exposed animals. Quantitative prediction of tissue residue still represents a challenging task despite long-term interest and pursuit, as tissue residue formation is the result of many dynamic j processes including uptake, transformation, and assimilation. The availability of high- I dimensional microarray gene expression data presents a new opportunity for computational predictive modeling of tissue residue from changes in expression profile. Here we analyzed a 240-sample data set with measurements of transcriptomic-wide gene expression and tissue residue of two chemicals, 2,4,6-trinitrotoluene (TNT) and l,3,5-trimtro-l,3,5-triazacyclo-hexane (RDX), in the earthworm Eisenia fetida. We applied two different computational approaches, LASSO (Least Absolute Shrinkage and Selection Operator) and RF (Random Forest), to identify predictor genes and built predictive models. Each approach was tested alone and in combination with a prior variable selection procedure that involved the Wilcoxon rank-sum test and HOPACH (Hierarchical Ordered Partitioning And Collapsing Hybrid). Model evaluation results suggest that LASSO was the best performer of minimum complexity on the TNT data set, whereas the combined Wifcoxon-HOPACH-RF approach achieved the highest prediction accuracy on the RDX data set. Our models separately identified two small sets of ca. 30 predictor genes for RDX and TNT. We have demonstrated that both LASSO and RF are powerful tools for quantitative prediction of tissue residue. They also leave more unknown than explained, however, allowing room for improvement with other computational methods and extension to mixture contamination scenarios.
机译：弹药厂和试验场附近的土壤污染是一个严重的环境问题，可能导致裸露的动物体内形成组织化学残留物。尽管有长期的关注和追求，但是组织残留的定量预测仍然是一项艰巨的任务，因为组织残留的形成是许多动态过程（包括摄取，转化和吸收）的结果。高I维微阵列基因表达数据的可用性为根据表达谱变化对组织残基进行计算预测建模提供了新的机会。在这里，我们分析了240个样本数据集，并测量了两种化学物质2,4,6-三硝基甲苯（TNT）和1,3,5-trimtro-1,3,5-triazacyclo的转录组基因表达和组织残留hexane（Eisenia fetida）中的正己烷（RDX）。我们应用了两种不同的计算方法，即LASSO（最小绝对收缩和选择算子）和RF（随机森林），以识别预测基因并建立预测模型。每种方法都经过单独测试，并与涉及Wilcoxon秩和检验和HOPACH（分层有序划分和折叠混合）的先验变量选择程序结合在一起进行了测试。模型评估结果表明，LASSO是TNT数据集最小复杂度的最佳执行者，而组合的Wifcoxon-HOPACH-RF方法在RDX数据集上实现了最高的预测精度。我们的模型分别确定了两个小的ca集合。 RDX和TNT的30个预测基因。我们已经证明，LASSO和RF都是用于定量预测组织残留的有力工具。他们还留下了比解释更多的未知数，但是，这为其他计算方法的改进和扩展到混合污染场景提供了空间。

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  • 来源
    《Environmental Science & Technology》 |2012年第1期|p.19-26|共8页
  • 作者

    Ping Gong; Po-Ru Loh; Natalie D. Barker; George Tucker; Nan Wang; Chenhua Zhang; B. Lynn Escalon; Bonnie Berger; Edward J. Perkins;

  • 作者单位

    Environmental Services, SpecPro inc., San Antonio, Texas, United States;

    Department of Mathematics and Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology,Cambridge, Massachusetts, United States;

    Environmental Services, SpecPro inc., San Antonio, Texas, United States;

    Department of Mathematics and Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology,Cambridge, Massachusetts, United States;

    School of Computing, University of Southern Mississippi, Hattiesburg, Mississippi, United States;

    Department of Mathematics, University of Southern Mississippi, Hattiesburg, Mississippi, United States;

    Environmental Laboratory, U.S. Army Engineer Research and Development Center, Vicksburg, Mississippi, United States;

    Department of Mathematics and Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology,Cambridge, Massachusetts, United States;

    Environmental Laboratory, U.S. Army Engineer Research and Development Center, Vicksburg, Mississippi, United States;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
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