Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.
Bioinformatics. 2010 Jun 1;26(11):1416-22. doi: 10.1093/bioinformatics/btq144. Epub 2010 Apr 7.
The identification of gene regulatory modules is an important yet challenging problem in computational biology. While many computational methods have been proposed to identify regulatory modules, their initial success is largely compromised by a high rate of false positives, especially when applied to human cancer studies. New strategies are needed for reliable regulatory module identification.
We present a new approach, namely multilevel support vector regression (ml-SVR), to systematically identify condition-specific regulatory modules. The approach is built upon a multilevel analysis strategy designed for suppressing false positive predictions. With this strategy, a regulatory module becomes ever more significant as more relevant gene sets are formed at finer levels. At each level, a two-stage support vector regression (SVR) method is utilized to help reduce false positive predictions by integrating binding motif information and gene expression data; a significant analysis procedure is followed to assess the significance of each regulatory module. To evaluate the effectiveness of the proposed strategy, we first compared the ml-SVR approach with other existing methods on simulation data and yeast cell cycle data. The resulting performance shows that the ml-SVR approach outperforms other methods in the identification of both regulators and their target genes. We then applied our method to breast cancer cell line data to identify condition-specific regulatory modules associated with estrogen treatment. Experimental results show that our method can identify biologically meaningful regulatory modules related to estrogen signaling and action in breast cancer.
The ml-SVR MATLAB package can be downloaded at http://www.cbil.ece.vt.edu/software.htm.
基因调控模块的鉴定是计算生物学中一个重要但具有挑战性的问题。虽然已经提出了许多计算方法来鉴定调控模块，但它们的初步成功在很大程度上受到高假阳性率的影响，尤其是在应用于人类癌症研究时。需要新的策略来进行可靠的调控模块鉴定。
我们提出了一种新的方法，即多层次支持向量回归（ml-SVR），用于系统地鉴定条件特异性调控模块。该方法基于一种多层次分析策略构建，旨在抑制假阳性预测。通过这种策略，随着更相关的基因集在更精细的层次上形成，调控模块变得更加显著。在每个层次上，使用两阶段支持向量回归（SVR）方法来帮助减少假阳性预测，方法是整合结合基序信息和基因表达数据；采用显著分析程序来评估每个调控模块的显著性。为了评估所提出策略的有效性，我们首先在模拟数据和酵母细胞周期数据上，将 ml-SVR 方法与其他现有方法进行了比较。所得结果表明，ml-SVR 方法在鉴定调控因子及其靶基因方面均优于其他方法。然后，我们将该方法应用于乳腺癌细胞系数据，以鉴定与雌激素处理相关的条件特异性调控模块。实验结果表明，我们的方法可以识别与乳腺癌中雌激素信号转导和作用相关的有生物学意义的调控模块。
ml-SVR MATLAB 软件包可在 http://www.cbil.ece.vt.edu/software.htm 下载。
