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The interaction between miRNA and lncRNA has rarely been studied in plants. So identification of potential miRNA-lncRNA interactions in various plant species remains a challenging task. Furthermore, among the identified miRNA-lncRNA interactions, only a few have been analyzed for biological significance experimentally. At present, lncRNA and miRNA discovered in animals and plants are difficult to verify through large-scale biological experiments. Therefore, there is an urgent need to use computational methods to reveal the characteristics of lncRNAs and miRNAs to guide those expensive and laborious laboratory experiments.
In order to deeply explore the interaction between corn miRNA and lncRNA, this article uses the PmliPred method.This method is based on hybrid models and fuzzy decision-making, combines deep learning and shallow machine learning, and uses original sequences and artificial extraction of characteristic plant miRNA-lncRNA to achieve classification prediction of miRNA-lncRNA interaction relationships. In order to achieve this goal, we first collect data, then preprocess the data and write a series of python programs to extract features from the data. PmliPred is then run for predictive analysis based on their sequences and features.
Using PmliPred software, we predicted putative interaction between maize lncRNAs and miRNAs. This allowed to reconstruct graph of the miRNA-lncRNA interacting pairs. Analysis of co-expression of the lncRNAs involved in the various interactions with miRNAs was also performed.
Our research provide new information about miRNA-lncRNA interaction in maize.