Cancer Gene Expression Data Attribute Partial Ordered Representation And Knowledge Discovery

In this paper, basic concepts and the related definitions of attribute partial-ordered structure diagram have been researched, and a scheme of knowledge discovery to collect new information from cancer gene expression data has been proposed, which was based on the feature selection and attribute partial-ordered structure diagram. Then the resource of lung adenocarcinoma gene expression data to be processed has been introduced in the paper. Both the T-test method and the Elastic net method have been used in the feature gene selection of lung adenocarcinoma gene expression data, and a total of 35 feature genes have been selected. This process sharply reduced the dimension of the data set. Finally, the c# program has been applied to disperse the data in order to generate the form of binary formal context, then the structural partial-ordered attribute diagram was generated, and the knowledge discovery has been produced based on the distribution and aggregation hierarchy diagram.