Chua Hon Nian, Hugo Willy, Liu Guimei, Li Xiaoli, Wong Limsoon, Ng See-Kiong
Data Mining Department, Institute for Infocomm Research, Singapore.
Ann N Y Acad Sci. 2009 Mar;1158:224-33. doi: 10.1111/j.1749-6632.2008.03760.x.
The protein-protein subnetwork prediction challenge presented at the 2nd Dialogue for Reverse Engineering Assessments and Methods (DREAM2) conference is an important computational problem essential to proteomic research. Given a set of proteins from the Saccharomyces cerevisiae (baker's yeast) genome, the task is to rank all possible interactions between the proteins from the most likely to the least likely. To tackle this task, we adopt a graph-based strategy to combine multiple sources of biological data and computational predictions. Using training and testing sets extracted from existing yeast protein-protein interactions, we evaluate our method and show that it can produce better predictions than any of the individual data sources. This technique is then used to produce our entry for the protein-protein subnetwork prediction challenge.
在第二届逆向工程评估与方法对话会（DREAM2）上提出的蛋白质-蛋白质子网预测挑战，是蛋白质组学研究中一个至关重要的计算问题。给定一组来自酿酒酵母基因组的蛋白质，任务是将这些蛋白质之间所有可能的相互作用按可能性从高到低进行排序。为解决此任务，我们采用基于图的策略来整合多种生物数据和计算预测来源。利用从现有的酵母蛋白质-蛋白质相互作用中提取的训练集和测试集，我们评估了我们的方法，并表明它能比任何单个数据源产生更好的预测。然后，这项技术被用于生成我们参与蛋白质-蛋白质子网预测挑战的参赛作品。
