Comprises a pipeline for predicting microRNAmRNA interactions as detailed in Williams Calinescu Mohorianu 2020 doi10.11012020.12.23.424130. Its input consists of a a messenger RNA mRNA dataset either in fasta format focused on 3 UTRs or in gtf format for the latter the sequences of the 3 UTRs are generated using the genomic coordinates b a microRNA dataset in fasta format retrieved from miRBase httpwww.mirbase.org and c an interaction dataset in csv format from miRTarBase httpmirtarbase.cuhk.edu.cnphpindex.php. To characterise and predict microRNAmRNA interactions we use a statistical analyses based on Chi-squared and Fisher exact tests and b Machine Learning classifiers decision trees random forests and support vector machines. To enhance the accuracy of the classifiers we also employ feature selection approaches used in on conjunction with the classifiers. The feature selection approaches include a voting scheme for decision trees a measure based on Gini index for random forests forward feature selection and Genetic Algorithms on SVMs. The pipeline also includes a novel approach based on embryonic Genetic Algorithms which combines and optimises the forward feature selection and Genetic Algorithms. All analyses including the classification and feature selection are applicable on the microRNA seed features default on the full microRNA features andor flanking features on the mRNA. The sets of features can be combined.