基于不确定性PPI网络的最大稠密子图挖掘*

研究表明使用PPI数据进行蛋白质功能预测是很有意义的。然而，从生物学实验得到的PPI数据一般是含有噪声的、不完全的和不精确的，这使得将PPI网络作为不确定图来处理变得更加合理。提出了一种基于深度优先搜索策略和点扩展的挖掘算法，它可以有效地从不确定的PPI网络中挖掘最大稠密子图。该算法使用了几种高效的剪枝技术来提高挖掘的时间效率。在酵母菌PPI数据上的实验结果表明该算法在精度和效率上都有很好的表现。

Mining maximal dense subgraphs in uncertain PPI network

Several studies have shown that the prediction of protein function using PPI data is promising. However, the PPI data generated from experiments are noisy, incomplete and inaccurate, which promotes to represent PPI dataset as an uncertain graph. This paper proposed a novel algorithm to mine maximal dense subgraphs efficiently in uncertain PPI network. It adopted several techniques to achieve efficient mining. An extensive experimental evaluation on yeast PPI network demonstrates that the approach has good performance in terms of precision and efficiency.