一种基于协同作用网络的特征模块搜索算法.

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. The interaction gain was used 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 can be obtained. The algorithm was validated on ten public data sets and showed advantages over the comparison algorithms in classification accuracy, sensitivity and specificity, which indicates the feature modules selected by the algorithm have better performance. [ABSTRACT FROM AUTHOR]