Institute of Statistical Science, Academia Sinica, Taipei 115, Taiwan and Institute of Biomedical Engineering, National Taiwan University, Taipei 106, Taiwan.
Bioinformatics. 2010 Feb 15;26(4):582-4. doi: 10.1093/bioinformatics/btp684. Epub 2009 Dec 10.
Inferring genetic or transcriptional interactions, when done successfully, may provide insights into biological processes or biochemical pathways of interest. Unfortunately, most computational algorithms require a certain level of programming expertise. To provide a simple web interface for users to infer interactions from time course gene expression data, we present WebPARE, which is based on the pattern recognition algorithm (PARE). For expression data, in which each type of interaction (e.g. activator target) and the corresponding paired gene expression pattern are significantly associated, PARE uses a non-linear score to classify gene pairs of interest into a few subclasses of various time lags. In each subclass, PARE learns the parameters in the decision score using known interactions from biological experiments or published literature. Subsequently, the trained algorithm predicts interactions of a similar nature. Previously, PARE was shown to infer two sets of interactions in yeast successfully. Moreover, several predicted genetic interactions coincided with existing pathways; this indicates the potential of PARE in predicting partial pathway components. Given a list of gene pairs or genes of interest and expression data, WebPARE invokes PARE and outputs predicted interactions and their networks in directed graphs.
推断遗传或转录相互作用，如果成功，可以深入了解感兴趣的生物过程或生化途径。不幸的是，大多数计算算法都需要一定程度的编程专业知识。为了为用户提供一个简单的网络界面，以便从时间过程基因表达数据中推断相互作用，我们提出了 WebPARE，它是基于模式识别算法 (PARE) 的。对于表达数据，其中每种相互作用（例如激活剂靶标）和相应的配对基因表达模式都有显著关联，PARE 使用非线性分数将感兴趣的基因对分类为几个具有不同时间滞后的子类。在每个子类中，PARE 使用来自生物实验或已发表文献中的已知相互作用来学习决策分数中的参数。随后，经过训练的算法预测具有类似性质的相互作用。以前，PARE 已成功推断出酵母中的两组相互作用。此外，一些预测的遗传相互作用与现有的途径一致；这表明 PARE 在预测部分途径成分方面具有潜力。给定感兴趣的基因对列表或基因以及表达数据，WebPARE 将调用 PARE，并以有向图的形式输出预测的相互作用及其网络。
