A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network.
A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network.
复制标题
一种通过结合功能基因网络的功能和拓扑特性来预测合成遗传相互作用的半监督学习方法
DOI：
10.1186/1471-2105-11-343
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发表时间：
2010-06-24
影响因子：
3
通讯作者：
Zhou X
中科院分区：
文献类型：
--
作者：
You ZH;Yin Z;Han K;Huang DS;Zhou X
遗传相互作用谱具有丰富的信息，有助于理解基因之间的功能联系，因此被广泛用于注释基因功能和解剖特定的途径结构。然而，我们的理解相当局限于双重同步扰动与各种更高水平的表型变化之间的关系，例如细胞，组织或器官中的表型变化。修饰筛选，如合成基因阵列（SGA）可以帮助我们了解由组合基因突变引起的表型。不幸的是，对任何基因组中所有可能的组合突变进行详尽的测试都容易受到组合爆炸的影响，并且在技术上或经济上都是不可行的。因此，一种精确的计算方法来预测遗传相互作用是非常必要的，这种方法有可能缓解实验设计的瓶颈。结果在本研究中，我们引入了一种计算系统生物学方法来准确预测成对合成遗传相互作用（SGI）。首先，通过整合蛋白-蛋白相互作用（PPI）、蛋白复合物和基因表达数据，构建高覆盖、高精度的功能基因网络（FGN）；然后，利用基于图的半监督学习（SSL）分类器识别SGI，其中加权FGN中蛋白质对的拓扑特性作为分类器的输入特征。在一个基准数据集inS上，我们将提出的SSL方法与最先进的监督分类器支持向量机（SVM）进行了比较。验证我们的方法区分合成基因相互作用和非相互作用基因对的能力。实验结果表明，该方法能准确预测遗传相互作用。酿酒酵母（灵敏度92%，特异性91%）。值得注意的是，SSL方法比SVM更有效，特别是对于非常小的训练集和大的测试集。结论我们开发了一个基于图的SSL分类器来预测SGI。该分类器使用加权FGN的拓扑属性作为输入特征，同时使用从标记和未标记数据中导出的信息。我们的分析表明，加权FGN的拓扑特性可以用来准确地预测SGI。此外，基于图的SSL方法优于传统的标准监督方法，特别是在与小训练集一起使用时。该方法减轻了穷举测试的实验负担，为生物学家筛选具有SGI的候选基因对提供了有益的指导。实现该方法的数据和源代码可从网站http://home.ustc.edu.cn/~yzh33108/GeneticInterPred.htm获得
BackgroundGenetic interaction profiles are highly informative and helpful for understanding the functional linkages between genes, and therefore have been extensively exploited for annotating gene functions and dissecting specific pathway structures. However, our understanding is rather limited to the relationship between double concurrent perturbation and various higher level phenotypic changes, e.g. those in cells, tissues or organs. Modifier screens, such as synthetic genetic arrays (SGA) can help us to understand the phenotype caused by combined gene mutations. Unfortunately, exhaustive tests on all possible combined mutations in any genome are vulnerable to combinatorial explosion and are infeasible either technically or financially. Therefore, an accurate computational approach to predict genetic interaction is highly desirable, and such methods have the potential of alleviating the bottleneck on experiment design.ResultsIn this work, we introduce a computational systems biology approach for the accurate prediction of pairwise synthetic genetic interactions (SGI). First, a high-coverage and high-precision functional gene network (FGN) is constructed by integrating protein-protein interaction (PPI), protein complex and gene expression data; then, a graph-based semi-supervised learning (SSL) classifier is utilized to identify SGI, where the topological properties of protein pairs in weighted FGN is used as input features of the classifier. We compare the proposed SSL method with the state-of-the-art supervised classifier, the support vector machines (SVM), on a benchmark dataset inS. cerevisiaeto validate our method's ability to distinguish synthetic genetic interactions from non-interaction gene pairs. Experimental results show that the proposed method can accurately predict genetic interactions inS. cerevisiae(with a sensitivity of 92% and specificity of 91%). Noticeably, the SSL method is more efficient than SVM, especially for very small training sets and large test sets.ConclusionsWe developed a graph-based SSL classifier for predicting the SGI. The classifier employs topological properties of weighted FGN as input features and simultaneously employs information induced from labelled and unlabelled data. Our analysis indicates that the topological properties of weighted FGN can be employed to accurately predict SGI. Also, the graph-based SSL method outperforms the traditional standard supervised approach, especially when used with small training sets. The proposed method can alleviate experimental burden of exhaustive test and provide a useful guide for the biologist in narrowing down the candidate gene pairs with SGI. The data and source code implementing the method are available from the website: http://home.ustc.edu.cn/~yzh33108/GeneticInterPred.htm