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In recent studies, non-coding protein RNAs have been identified as microRNAthat can be used as biomarkers for early diagnosis and treatment of cancer,that decrease mortality in cancer. A microRNA may target hundreds or thousandsof genes and a gene may regulate several microRNAs, so determining whichmicroRNA is associated with which cancer is a big challenge. Many computationalmethods have been performed to detect micoRNAs association with cancer, butmore effort is needed with higher accuracy. Increasing research has shown thatrelationship between microRNAs and TFs play a significant role in the diagnosisof cancer. Therefore, we developed a new computational framework (CAMIRADA) toidentify cancer-related microRNAs based on the relationship between microRNAsand disease genes (DG) in the protein network, the functional relationshipsbetween microRNAs and Transcription Factors (TF) on the co-expression network,and the relationship between microRNAs and the Differential Expression Gene(DEG) on co-expression network. The CAMIRADA was applied to assess breastcancer data from two HMDD and miR2Disease databases. In this study, the AUC forthe 65 microRNAs of the top of the list was 0.95, which was more accurate thanthe similar methods used to detect microRNAs associated with the cancer artery.

CAMIRADA: Cancer microRNA association discovery algorithm, a case study on breast cancer