The LeFE algorithm: embracing the complexity of gene expression in the interpretation of microarray data.
The LeFE algorithm: embracing the complexity of gene expression in the interpretation of microarray data.
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DOI：
10.1186/gb-2007-8-9-r187
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发表时间：
2007
期刊：
影响因子：
12.3
通讯作者：
Weinstein JN
中科院分区：
文献类型：
--
作者：
Eichler GS;Reimers M;Kane D;Weinstein JN
LeFE算法已被开发用于解决基因表达的复杂的非线性调节。微阵列数据的解释仍然是一个挑战，大多数方法未能考虑基因表达的复杂，非线性调控。为了解决这一限制，我们引入了基于随机森林的统计/机器学习算法Learner of Functional Enrichment（LeFE），并在几个不同的数据集上进行了演示：吸烟者/从不吸烟者，乳腺癌分类和癌症药物敏感性。我们还将其与以前发表的算法进行了比较，包括基因集富集分析。LeFE定期识别与已知生物学一致的统计学显著功能主题。
The LeFE algorithm has been developed to address the complex, non-linear regulation of gene expression. Interpretation of microarray data remains a challenge, and most methods fail to consider the complex, nonlinear regulation of gene expression. To address that limitation, we introduce Learner of Functional Enrichment (LeFE), a statistical/machine learning algorithm based on Random Forest, and demonstrate it on several diverse datasets: smoker/never smoker, breast cancer classification, and cancer drug sensitivity. We also compare it with previously published algorithms, including Gene Set Enrichment Analysis. LeFE regularly identifies statistically significant functional themes consistent with known biology.