Journal of Cybersecurity and Information Management JCIM 2690-6775 2769-7851 10.54216/JCIM https://www.americaspg.com/journals/show/361 2019 2019 Post-Doctoral position, University of Tunis El Manar, LR99ES10 Human Genetics Laboratory, Faculty of Medicine of Tunis (FMT), Tunisia Rabeb Touati Post-Doctoral position, University of Tunis El Manar, LR99ES10 Human Genetics Laboratory, Faculty of Medicine of Tunis (FMT), Tunisia Dr. Imen Ferchichi Assiatnt Professor, University of Carthage, Higher Institute of Information Technologies and Communications, Industrial Computing Department, Tunisia Dr. Imen Messaoudi Associate Professor, University of Carthage, National School of Engineers of Cartage, Electrical Engineering Department, Tunisia Dr.Afef Elloumi Oueslati Professor, University Tunis El Manar, SITI Laboratory, National School of Engineers of Tunis, Tunisia Dr. Zied Lachiri The first MicroRNAs was discovered 27 years ago in the nematode C.elegans genomes. MicroRNAs (miRNAs) sequences are small and are expressed in various genomes to affect the translation or the stability of target mRNAs. These short RNA sequences are involved in targeting post-transcriptional gene regulation. The mature miRNAs are derived from longer sequence precursors (pre-miRNAs). Previous works have shown that pre-miRNAs can be classified by their species of origin using bioinformatics techniques combined with machine learning tools. In this study, we focus on the classification of Precursor microRNAs sequences, from 16 different species ranging from animals, plants, and viruses, based on the combination of the features extracted from images corresponding to DNA sequences and machine learning algorithms. As a result, our classification shows that the system based on features correspond to energy images of pre-miRNAs signals using the PNUC coding technique corresponding to the DNA sequence is very efficient in terms of miRNAs inter-genomics recognition 2020 2020 05 13 10.54216/JCIM.030101 https://www.americaspg.com/articleinfo/2/show/361