Transcriptional network classifiers.
Transcriptional network classifiers.
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DOI：
10.1186/1471-2105-10-s9-s1
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发表时间：
2009-09-17
影响因子：
3
通讯作者：
Ramoni MF
中科院分区：
文献类型：
--
作者：
Chang HH;Ramoni MF
基因相互作用在转录网络中发挥着核心作用。许多研究进行了全基因组表达分析，以重建调控网络来研究疾病过程。由于生物过程是调控基因相互作用的结果，本文开发了一种系统生物学方法来推断调节表型性状的功能依赖性转录网络，该网络可作为识别组织状态的分类器。由于分析中考虑了基因相互作用，我们可以获得比现有方法更高的分类精度。我们的系统生物学方法是通过贝叶斯网络框架进行的。该算法包括两个步骤：通过贝叶斯因子进行基因过滤，然后通过网络学习消除共线性。我们用两个临床数据验证了我们的方法。在肺癌亚型区分的研究中，我们从111个训练样本中得到了25个基因的分类器，在422个独立样本上的测试达到了95%的分类准确率。在胸主动脉瘤（TAA）诊断研究中，61个样本确定了34个基因的分类器，对33个独立样本的诊断准确率达到82%。与其他三种流行方法（PCA/LDA、PAM 和加权投票）的性能比较证实，我们的方法可产生卓越的分类精度和更紧凑的签名。本文提出的系统生物学方法能够推断功能依赖的转录网络，从而可以高精度地对生物样本进行分类。使用临床数据对我们的分类器进行验证证明了我们提出的疾病诊断方法的有希望的价值。
Gene interactions play a central role in transcriptional networks. Many studies have performed genome-wide expression analysis to reconstruct regulatory networks to investigate disease processes. Since biological processes are outcomes of regulatory gene interactions, this paper develops a system biology approach to infer function-dependent transcriptional networks modulating phenotypic traits, which serve as a classifier to identify tissue states. Due to gene interactions taken into account in the analysis, we can achieve higher classification accuracy than existing methods. Our system biology approach is carried out by the Bayesian networks framework. The algorithm consists of two steps: gene filtering by Bayes factor followed by collinearity elimination via network learning. We validate our approach with two clinical data. In the study of lung cancer subtypes discrimination, we obtain a 25-gene classifier from 111 training samples, and the test on 422 independent samples achieves 95% classification accuracy. In the study of thoracic aortic aneurysm (TAA) diagnosis, 61 samples determine a 34-gene classifier, whose diagnosis accuracy on 33 independent samples achieves 82%. The performance comparisons with three other popular methods, PCA/LDA, PAM, and Weighted Voting, confirm that our approach yields superior classification accuracy and a more compact signature. The system biology approach presented in this paper is able to infer function-dependent transcriptional networks, which in turn can classify biological samples with high accuracy. The validation of our classifier using clinical data demonstrates the promising value of our proposed approach for disease diagnosis.