MicroRNAs(miRNAs)are a class of non-coding single-stranded RNA molecules encoded by endogenous genes,with a length of about 22 nucleotides.They participate in post-transcriptional gene expression regulation in animals and plants.Numerous studies have shown that miRNAs play crucial roles in the occurrence and development of various complex diseases,including tumors.Therefore,identifying disease-related miRNAs is of significant importance for studying disease mecha-nisms and developing appropriate treatment strategies.Given the time-consuming and costly nature of wet-lab validation methods,many researchers focus on developing efficient computational models to i-dentify novel miRNA-disease associations.This study proposes a data-driven model based on autoen-coders to predict miRNA-disease associations.The results indicate that the disease-related miRNAs predicted are significantly enriched in the list of disease-related miRNAs from the HMDD database.Furthermore,by analyzing the top-ranked miRNAs,it was found that these miRNAs perform crucial biological functions and accurately exhibit disease classification.In conclusion,the model proposed in this paper serves as a valuable auxiliary tool for the discovery of disease-related miRNAs.
关键词
自编码器/miRNA-disease关联/数据驱动模型
Key words
autoencoders/miRNA-disease associations/data-driven model
维普
万方数据