A classification-based framework for predicting and analyzing gene regulatory response.
A classification-based framework for predicting and analyzing gene regulatory response.
复制标题
一个基于分类的框架，用于预测和分析基因调节反应。
DOI：
10.1186/1471-2105-7-s1-s5
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发表时间：
2006-03-20
影响因子：
3
通讯作者：
Leslie, C
中科院分区：
文献类型：
--
作者：
Kundaje, A;Middendorf, M;Shah, M;Wiggins, CH;Freund, Y;Leslie, C
我们最近推出了一个预测框架，用于研究基因转录调控在简单的生物体中使用一种新的监督学习算法称为GeneClass。GeneClass的动机是这样一个假设，即在模式生物如酿酒酵母中，我们可以学习一个决策规则，用于预测一个基因是否在特定的微阵列实验中上调或下调，这是基于基因调控区中结合位点的存在（“基序”）和实验中转录因子等调控因子的表达水平（“亲本”）。GeneClass将学习任务制定为分类问题-预测与微阵列测量中的生物和测量噪声水平之外的上调和下调相对应的+1和-1标签。使用Adaboost算法，GeneClass以交替决策树的形式学习预测函数，这是决策树的基于边缘的泛化。在目前的工作中，我们引入了一个新的，强大的版本的GeneClass算法，提高了稳定性和计算效率，产生了一个更可扩展和可靠的预测模型。预测树的稳定性提高，使我们能够引入一个详细的后处理框架的生物学解释，包括个人和群体的目标基因分析，以揭示特定条件下的调控程序，并建议信号通路。Robust GeneClass使用了一种新的稳定的boosting变体，允许在树的节点上包含一组相关特征，而不是单个特征;通过这种方式，与单个最佳特征相关的生物学重要特征被保留，而不是在下一轮boosting中去相关和丢失。其他计算方面的发展包括所有特征的损失函数的快速矩阵计算，允许大数据集的可扩展性，以及使用弃权弱规则，这导致更浅和可解释的树。我们还展示了如何将来自ChIP芯片实验的全基因组蛋白质-DNA结合数据纳入GeneClass算法，并且我们使用改进的基因表达数据噪声模型。使用Robust GeneClass改进的可扩展性，我们在酵母环境压力数据集上进行了更大规模的实验，对所有基因进行了训练和测试，并使用了一套全面的潜在调节剂。我们证明了改进的稳定性的学习预测树中的功能，我们通过分析酵母中的两组基因-蛋白伴侣和一组推定的Nrg 1和Nrg 2转录因子的目标-并提出新的假设，他们的转录和转录后调控显示的实用程序的后处理框架。详细的结果和健壮的基因类源代码可从下载。
We have recently introduced a predictive framework for studying gene transcriptional regulation in simpler organisms using a novel supervised learning algorithm called GeneClass. GeneClass is motivated by the hypothesis that in model organisms such as Saccharomyces cerevisiae, we can learn a decision rule for predicting whether a gene is up- or down-regulated in a particular microarray experiment based on the presence of binding site subsequences ("motifs") in the gene's regulatory region and the expression levels of regulators such as transcription factors in the experiment ("parents"). GeneClass formulates the learning task as a classification problem — predicting +1 and -1 labels corresponding to up- and down-regulation beyond the levels of biological and measurement noise in microarray measurements. Using the Adaboost algorithm, GeneClass learns a prediction function in the form of an alternating decision tree, a margin-based generalization of a decision tree. In the current work, we introduce a new, robust version of the GeneClass algorithm that increases stability and computational efficiency, yielding a more scalable and reliable predictive model. The improved stability of the prediction tree enables us to introduce a detailed post-processing framework for biological interpretation, including individual and group target gene analysis to reveal condition-specific regulation programs and to suggest signaling pathways. Robust GeneClass uses a novel stabilized variant of boosting that allows a set of correlated features, rather than single features, to be included at nodes of the tree; in this way, biologically important features that are correlated with the single best feature are retained rather than decorrelated and lost in the next round of boosting. Other computational developments include fast matrix computation of the loss function for all features, allowing scalability to large datasets, and the use of abstaining weak rules, which results in a more shallow and interpretable tree. We also show how to incorporate genome-wide protein-DNA binding data from ChIP chip experiments into the GeneClass algorithm, and we use an improved noise model for gene expression data. Using the improved scalability of Robust GeneClass, we present larger scale experiments on a yeast environmental stress dataset, training and testing on all genes and using a comprehensive set of potential regulators. We demonstrate the improved stability of the features in the learned prediction tree, and we show the utility of the post-processing framework by analyzing two groups of genes in yeast — the protein chaperones and a set of putative targets of the Nrg1 and Nrg2 transcription factors — and suggesting novel hypotheses about their transcriptional and post-transcriptional regulation. Detailed results and Robust GeneClass source code is available for download from .