Based on bioinformatics, differentially expressed gene data of drug-resistance in gastric
cancer were analyzed, screened and mined through modeling and network modeling to find
valuable data associated with multi-drug resistance of gastric cancer. First, data sets were
preprocessed from three aspects: data processing, data annotation and classification, and functional
clustering. Secondly, based on the preprocessed data, each classified primary gene regulatory
network was constructed by mining interactions among the genes. This paper computed the values
of each node in each classified primary gene regulatory network and ranked these nodes according
to their scores. On the basis of this, the appropriate core node was selected and the corresponding
core network was developed. Finally, core network modules were analyzed, which were mined.
After the correlation analysis, the result showed that the constructed network module had 20 core
genes. This module contained valuable data associated with multi-drug resistance in gastric cancer.