Volume 3 , Issue 1 , PP: 05-13, 2020 | Cite this article as | XML | Html | PDF | Full Length Article
Rabeb Touati 1 * , Dr. Imen Ferchichi 2 , Dr. Imen Messaoudi 3 , Dr.Afef Elloumi Oueslati 4 , Dr. Zied Lachiri 5
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
microRNA, Precursor microRNA, Features, scalogram, wavelet-energy, Classification
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