A pattern recognition approach to infer time-lagged genetic interactions
A pattern recognition approach to infer time-lagged genetic interactions
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DOI：
10.1093/bioinformatics/btn098
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发表时间：
2008-05-01
期刊：
影响因子：
5.8
通讯作者：
Shieh, Grace S.
中科院分区：
文献类型：
--
作者：
Chuang, Cheng-Long;Jen, Chih-Hung;Shieh, Grace S.
动机：对于任何基因相互作用及相关配对模式具有依赖性的时间进程微阵列数据，所提出的模式识别（PARE）方法能够推断具有时间滞后的基因相互作用，由于时间点数量少且基因数量大，这是一项具有挑战性的任务。PARE利用非线性评分来识别具有不同时间滞后的基因对亚类。在每个亚类中，PARE提取配对基因表达曲线的非线性特征，并应用优化算法从生物实验或已发表文献中的一些已知相互作用的微阵列基因表达数据（MGED）中学习决策评分的权重。也就是说，PARE通过机器学习整合MGED和现有知识，随后预测该亚类中的其他基因相互作用。
结果：PARE、一种时间滞后相关方法以及图形高斯模型的最新进展被用于预测112（132）对TC/TD（转录调控）相互作用。与qRT - PCR结果（已发表文献）进行核对，它们的真阳性率分别为73（77）、46（51）和52（59）。在酵母基因组中预测TC和TD（AT和RT）相互作用的假阳性率分别被限制在13和10（10和14）以内。一些预测的TC/TD相互作用被证明与涉及Sgs1、Srs2和Mus81的现有通路相符。这增加了应用基因相互作用预测蛋白质复合物通路的可能性。此外，还预测了一些涉及DNA修复的可进行实验验证的基因相互作用。
可用性：补充数据和PARE软件可在http://www.stat.sinica.edu.tw/类似于gshieh/pare.htm获取。
联系人：gshieh@stat.sinica.edu.tw
Motivation: For any time-course microarray data in which the gene interactions and the associated paired patterns are dependent, the proposed pattern recognition (PARE) approach can infer time-lagged genetic interactions, a challenging task due to the small number of time points and large number of genes. PARE utilizes a non-linear score to identify subclasses of gene pairs with different time lags. In each subclass, PARE extracts non-linear characteristics of paired gene-expression curves and learns weights of the decision score applying an optimization algorithm to microarray gene-expression data (MGED) of some known interactions, from biological experiments or published literature. Namely, PARE integrates both MGED and existing knowledge via machine learning, and subsequently predicts the other genetic interactions in the subclass.Results: PARE, a time-lagged correlation approach and the latest advance in graphical Gaussian models were applied to predict 112 (132) pairs of TC/TD (transcriptional regulatory) interactions. Checked against qRT-PCR results (published literature), their true positive rates are 73 (77), 46 (51), and 52 (59), respectively. The false positive rates of predicting TC and TD (AT and RT) interactions in the yeast genome are bounded by 13 and 10 (10 and 14), respectively. Several predicted TC/TD interactions are shown to coincide with existing pathways involving Sgs1, Srs2 and Mus81. This reinforces the possibility of applying genetic interactions to predict pathways of protein complexes. Moreover, some experimentally testable gene interactions involving DNA repair are predicted.Availability: Supplementary data and PARE software are available at http://www.stat.sinica.edu.tw/similar to gshieh/pare.htm.Contact: gshieh@stat.sinica.edu.tw.