Classification of early-stage non-small cell lung cancer by weighing gene expression profiles with connectivity information
Classification of early-stage non-small cell lung cancer by weighing gene expression profiles with connectivity information
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通过权衡基因表达谱与连接信息对早期非小细胞肺癌进行分类
DOI：
10.1002/bimj.201700010
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发表时间：
2017
影响因子：
1.7
通讯作者：
Suyan Tian
中科院分区：
文献类型：
--
作者：
Ao Zhang;Suyan Tian
基于通路的特征选择算法利用通路中包含的生物信息来指导应该选择哪些特征/基因，已经迅速发展并在生物信息学领域变得广泛。根据路径信息的整合方式，我们将基于路径的特征选择算法分为三大类-惩罚，逐步向前和加权。与前两类相比，加权方法虽然通常是最简单的方法，但却没有得到充分利用。在这篇文章中，我们为每个基因构建了三个不同的基于基因连接信息的权重，然后对所得的加权基因表达谱进行特征选择。使用模拟和真实的世界应用，我们已经证明，当考虑从研究中的特定疾病的数据构建的数据驱动的连接信息时，所得的加权基因表达谱略微优于原始表达谱。综上所述，加权方法面临的一大挑战是如何更准确和精确地估计基于路径知识的权重。只有成功地克服了这个问题，加权方法才不可能得到广泛应用。
Pathway‐based feature selection algorithms, which utilize biological information contained in pathways to guide which features/genes should be selected, have evolved quickly and become widespread in the field of bioinformatics. Based on how the pathway information is incorporated, we classify pathway‐based feature selection algorithms into three major categories—penalty, stepwise forward, and weighting. Compared to the first two categories, the weighting methods have been underutilized even though they are usually the simplest ones. In this article, we constructed three different genes’ connectivity information‐based weights for each gene and then conducted feature selection upon the resulting weighted gene expression profiles. Using both simulations and a real‐world application, we have demonstrated that when the data‐driven connectivity information constructed from the data of specific disease under study is considered, the resulting weighted gene expression profiles slightly outperform the original expression profiles. In summary, a big challenge faced by the weighting method is how to estimate pathway knowledge‐based weights more accurately and precisely. Only until the issue is conquered successfully will wide utilization of the weighting methods be impossible.