Abstract: Cancer is the first killer of human health.With the rapid development of sequencing technology,a massive amount of cancer gene expression data has been accumulated,and using computational methods to predict pathogenic genes has become a new hotspot in cancer research.However,currently,the prediction of pathogenic genes is mostly based on gene interaction networks,and little consideration is given to the potential connection between local network connections and differential gene expression.In response to the above issues,this paper first utilizes gene expression difference data before and after the disease,calculates the correlation between genes through mutual information,and constructs an adjacency network.Then,a feature vector model is designed for predicting cancer pathogenic genes.Vector features include differential expression information of candidate genes and their neighbors.Cancer-related pathogenic and non pathogenic genes are obtained from public databases such as TCGA,OMIM,and GEO,as well as differential expression data of genes before and after illness,for experiments.Differential expression information of genes and their neighbors in adjacency networks are used for cancer pathogenic gene prediction(DICPG).The experimental results show that the DICPG cancer gene classification model has significant biological significance,and its classification accuracy and AUC performance indicators are superior to similar methods.

Key words: Gene differential expression data, Adjacent network, Candidate gene, Gene feature vector, Cancer pathogenic gene prediction