基于不确定PPI网络的功能模块挖掘

Mining functional modules in uncertain protein-protein interaction network

近年来,挖掘具有生物学意义的功能模块,吸引了很多人的关注。但是,生物信息学中的蛋白质交互(PPI)网络和其他的一些生物数据常常会由于实验检测方法的局限性而呈现出不确定性。以具有不确定性的PPI数据为研究对象,挖掘蛋白质复合物。引入了一些新概念,并给出了一个深度优先算法。使用MIPS数据库评估实验结果表明,该算法在精确度和覆盖率两个方面性能优良。在基因拓扑上分析实验结果证实了所得到的大多数蛋白质复合物具有很高的相似性。最后也对算法的可扩展性进行了验证。总之,可以有效地从不确定PPI网络中挖掘出功能模块。

Mining functional modules with biological significance has attracted lots of attention recently. However, protein-pro- tein interaction （PPI） network and other biological data generally bear uncertainties attributed to noise, incompleteness and inaccuracy in practice. This paper focused on received uncertain PPI data to explore interesting protein complexes. Moreover, used some novel conceptions extended from known graph conceptions to develop a depth-first algorithm to mine protein comple- xes in a simple uncertain graph. Experiments took protein complexes from MIPS database as standard of accessing experimental results. Experiment results indicate that the algorithm has good performance in terms of coverage and precision. Experimental results are also assessed on gene ontology （GO） annotation, and the evaluation demonstrates proteins of most acquired protein complexes show a high similarity. Finally, several experiments are taken to test the scalability of the algorithm. It come to a conclusion that it can effectively mine functional modules from uncertain PPI network.