一种基于协同作用网络的特征模块搜索算法

如何利用数据挖掘领域的特征选择技术，从高维复杂的组学数据中提取关键特征一直是研究重点。对此，针对组学数据特征间存在的复杂关联关系进行研究，提出了基于协同作用网络的特征模块搜索算法。该算法利用交互增益值构建协同作用网络，通过衡量候选节点与当前特征模块连接的紧密程度，同时结合节点自身分类性能实现模块搜索，确定重要特征。在十个数据集上对该算法的性能进行了测试分析，在分类准确率、灵敏度、特异性三项指标上该算法与对比算法相比均有优势，这表明其所确定的网络模块性能更优。

Feature module selection algorithm based on synergetic network

Using feature selection technology in data mining to define the key features from high-dimensional and complex omics data has been the focus of research. This paper studied the complex correlation between features of the omics data, and proposed a feature module selection algorithm based on the synergetic network. It used the interaction gain to construct the weighted synergetic network. By measuring the closeness of candidate nodes to the current feature module and combining with the node''s own classification performance, important feature module could be obtained. It validated the algorithm on ten public data sets. The algorithm showes advantages over the comparison algorithms in classification accuracy, sensitivity and specificity, which indicates the feature modules selected by the algorithm have better performance.