基于关联基因本体论注释的蛋白质相互作用预测

细胞中的生理活动主要是通过蛋白质 - 蛋白质之间的相互作用来调控完成 . 详尽细致的蛋白质 - 蛋白质相互作用网络的解析对于理解细胞中复杂的调控、代谢和信号通路有重要的意义 . 近年来，关于新的蛋白质 - 蛋白质相互作用预测领域进展快速，这里，利用贝叶斯算法结合关联的 GO (Gene Ontology) ，来预测蛋白质的相互作用 . 利用非冗余的蛋白质相互作用数据来观察 GO 对的特性，得到 GO 关联的概率 . 通过阳性的和阴性的标准对照数据证实这个新方法可以很好地区别这两类不同的数据，显示出较好的灵敏度和非常低的假阳性预测率 . 通过与已知的高通量的实验数据比较，这个方法具有灵敏度高、速度快的优点 . 而且，运用这个新方法可以提供一些新的关于细胞内蛋白质之间相互作用的信息，为进一步的实验提供理论依据 .

Protein-protein Interaction Prediction With Correlated Gene Ontology

The cellular processes in cells are controlled by protein-protein interactions (PPI), and comprehensive PPI maps are important to understand the complicated regulatory, metabolic and signaling pathways. Recently, new parameters for PPI prediction are under discovering. Here, a Na?ve Bayesian algorithm with the correlated Gene Ontology (GO) was used for PPI prediction. The characteristic pairs of GO terms was demonstrated by training a non-redundant PPI data set from two online budding yeast databases, and the probability about this two correlated GO terms was also obtained. The accuracy of the prediction was tested by both positive and negative control data. The approach can distinguish them properly, with a satisfied sensitivity and low false positive rate. After comparing the prediction result to the data derived from high-throughput experiments, it is proved that the method is more sensitive and efficient than other means. Furthermore, some new insightful knowledge about interactions of the proteins will be found using this prediction approach, and the prediction is very helpful to the laboratory experiments.