Dipartimento di Area Critica Medico-Chirurgica, Università degli Studi di Firenze, Firenze, Italy.
PLoS One. 2012;7(6):e38767. doi: 10.1371/journal.pone.0038767. Epub 2012 Jun 28.
Predicting the biological function of all the genes of an organism is one of the fundamental goals of computational system biology. In the last decade, high-throughput experimental methods for studying the functional interactions between gene products (GPs) have been combined with computational approaches based on Bayesian networks for data integration. The result of these computational approaches is an interaction network with weighted links representing connectivity likelihood between two functionally related GPs. The weighted network generated by these computational approaches can be used to predict annotations for functionally uncharacterized GPs. Here we introduce Weighted Network Predictor (WNP), a novel algorithm for function prediction of biologically uncharacterized GPs. Tests conducted on simulated data show that WNP outperforms other 5 state-of-the-art methods in terms of both specificity and sensitivity and that it is able to better exploit and propagate the functional and topological information of the network. We apply our method to Saccharomyces cerevisiae yeast and Arabidopsis thaliana networks and we predict Gene Ontology function for about 500 and 10000 uncharacterized GPs respectively.
预测生物体所有基因的生物学功能是计算系统生物学的基本目标之一。在过去的十年中，研究基因产物 (GP) 之间功能相互作用的高通量实验方法与基于贝叶斯网络的数据集成计算方法相结合。这些计算方法的结果是一个带有加权链接的相互作用网络，这些链接代表两个功能相关的 GP 之间的连接可能性。这些计算方法生成的加权网络可用于预测功能未知的 GP 的注释。在这里，我们介绍了加权网络预测器 (WNP)，这是一种用于预测生物学上未知 GP 功能的新算法。在模拟数据上进行的测试表明，WNP 在特异性和敏感性方面均优于其他 5 种最先进的方法，并且能够更好地利用和传播网络的功能和拓扑信息。我们将我们的方法应用于酿酒酵母和拟南芥网络，分别预测了约 500 和 10000 个未知 GP 的基因本体功能。
