Objective: 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.
Methods: 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.
Results and Conclusion: 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.