MicroRNAs are small non-coding RNAs with a pivotal role in morphine addiction and tolerance. However, little is known about specific microRNAs involved in regulating molecular mechanisms of addiction. Therefore, the identification of microRNAs targeting specific genes involved in addiction through computational methods could be very helpful in reducing the time and cost of laboratory methods. The aim of this study is to investigate the possible connections between microRNAs and genes involved in morphine addiction in rats by using bioinformatics tools. In this way, high probable communications that will be useful for purposeful future laboratory studies will be identified and proposed. First, a list of microRNAs and genes involved in morphine addiction was collected by searching in NCBI database and 30 microRNAs and 144 related genes were selected. Then, links between the genes and the microRNAs involved in morphine addiction in rats were found using MicroRNA Target Prediction Database (www.mirdb.org). Thereafter, the desired connections were modeled in the form of a bipartite network, the relationships between microRNA and genes was graphed, and then a customized version of link prediction algorithms was set to find the most probable relations between the related microRNAs and target genes. Further, the accuracy of the results was calculated and confirmed by measuring the AUC. Finally, the 20 top relations predicted were reported in order of priority. new relation between rno-miR-1-3p, rno-miR-1b, rno-miR-9a-5b, rno-miR-16-5p, rno-miR-27a-3p, rno-miR-132-3p, rno-miR-19b-3p and rno-miR-212-3p recpectively with Ms4a7, Pi4ka, Creb1, Avpr1a, Cxcr5, Slain2, Creb1 and Tdrd7 are predicted. These computational findings are the most promising connections that are likely to exist but have not yet been reported in the online databases. They are the best choices to be validated using in vitro studies. Therefore, a proposed way is to perform related laboratory tests to confirm or disprove such predictions.
