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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >Gene Selection Using Iterative Feature Elimination Random Forests for Survival Outcomes
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Gene Selection Using Iterative Feature Elimination Random Forests for Survival Outcomes

机译：使用迭代特征消除随机森林进行生存结果的基因选择

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摘要

Although many feature selection methods for classification have been developed, there is a need to identify genes in high-dimensional data with censored survival outcomes. Traditional methods for gene selection in classification problems have several drawbacks. First, the majority of the gene selection approaches for classification are single-gene based. Second, many of the gene selection procedures are not embedded within the algorithm itself. The technique of random forests has been found to perform well in high-dimensional data settings with survival outcomes. It also has an embedded feature to identify variables of importance. Therefore, it is an ideal candidate for gene selection in high-dimensional data with survival outcomes. In this paper, we develop a novel method based on the random forests to identify a set of prognostic genes. We compare our method with several machine learning methods and various node split criteria using several real data sets. Our method performed well in both simulations and real data analysis. Additionally, we have shown the advantages of our approach over single-gene-based approaches. Our method incorporates multivariate correlations in microarray data for survival outcomes. The described method allows us to better utilize the information available from microarray data with survival outcomes.
机译：尽管已经开发了许多用于分类的特征选择方法，但仍需要在具有审查的生存结果的高维数据中鉴定基因。分类问题中传统的基因选择方法有几个缺点。首先，用于分类的大多数基因选择方法都是基于单基因的。其次，许多基因选择程序并未嵌入算法本身。已经发现，随机森林技术在具有生存结果的高维数据环境中表现良好。它还具有嵌入式功能，可识别重要变量。因此，它是具有生存结果的高维数据中基因选择的理想人选。在本文中，我们开发了一种基于随机森林的新方法来鉴定一组预后基因。我们使用几种实际数据集将我们的方法与几种机器学习方法和各种节点拆分标准进行比较。我们的方法在模拟和真实数据分析中均表现出色。此外，我们已经展示了我们的方法比基于单基因的方法的优势。我们的方法将微阵列数据中的多元相关性纳入了生存结果。所描述的方法使我们能够更好地利用可从微阵列数据获得的信息并获得生存结果。

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