MicroRNA-targeted gene prediction based on TransformerMGI
Biological small molecules such as microRNAs play an important role in biological processes and can positively or negatively regulate gene expression.Studying the relationship between microRNAs and genes is of great significance for the maintenance of homeostasis and the treatment of diseases.In this paper,the deep learning method is used to predict the relationship between microRNA and gene targeting,and the TransformerMGI model is proposed.In the feature engineering stage,aiming at the problem that it is difficult to extract the potential information of biological sequences accurately,the GP-GCN method based on graph convolutional neural network and DNA2Vec model are used to extract the potential information of microRNA and gene data,respectively,and the representation embedding matrix of microRNA and gene data is obtained.In terms of models,the TransformerMGI model introduces power normalization to improve the classical deep learning models.After feature extraction of microRNA and gene data in this paper,two representation matrices are obtained,which are respectively put into the TransformerMGI model.Through the internal Attention mechanism of the TransformerMGI model,the self-and mutual feature information of microRNA and gene are aggregated and correlated.Finally,the probability of microRNA regulating genes is predicted.In this paper,the area under the ROC curve and the exact recall curve are used as the performance evaluation indicators to evaluate the proposed model compared with other existing models.The experimental results show that the AUC and AUPRC scores of the TransformerMGI model proposed in this paper can reach more than 0.91,which is better than other existing models.The TransformerMGI model can only rely on the base sequence information of microRNA and gene without considering the biological principles and genomic background,and realize the prediction of microRNA target genes,which provides a useful deep learning method for the subsequent prediction of microRNA target genes.
国家科技期刊平台
NSTL
万方数据
