Briefings in Bioinformatics, Volume (24), No (3), Year (2023-5)

Title : ( DiMo: discovery of microRNA motifs using deep learning and motif embedding )

Authors: Fatemeh Farhadi , Mohammad Allahbakhsh , Ali Maghsoudi , Nadia Armin , Haleh Amintoosi ,

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MicroRNAs are small regulatory RNAs that decrease gene expression after transcription in various biological disciplines. In bioinformatics, identifying microRNAs and predicting their functionalities is critical. Finding motifs is one of the most well-known and important methods for identifying the functionalities of microRNAs. Several motif discovery techniques have been proposed, some of which rely on artificial intelligence-based techniques. However, in the case of few or no training data, their accuracy is low. In this research, we propose a new computational approach, called DiMo, for identifying motifs in microRNAs and generally macromolecules of small length. We employ word embedding techniques and deep learning models to improve the accuracy of motif discovery results. Also, we rely on transfer learning models to pre-train a model and use it in cases of a lack of (enough) training data. We compare our approach with five state-of-the-art works using three real-world datasets. DiMo outperforms the selected related works in terms of precision, recall, accuracy and f1-score.


, Motif Discovery, NLP, miRNA Motif Discovery, Motif Finding, Deep Learning
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author = {Fatemeh Farhadi and Allahbakhsh, Mohammad and Ali Maghsoudi and Armin, Nadia and Amintoosi, Haleh},
title = {DiMo: discovery of microRNA motifs using deep learning and motif embedding},
journal = {Briefings in Bioinformatics},
year = {2023},
volume = {24},
number = {3},
month = {May},
issn = {1467-5463},
keywords = {Motif Discovery; NLP; miRNA Motif Discovery; Motif Finding; Deep Learning},


%0 Journal Article
%T DiMo: discovery of microRNA motifs using deep learning and motif embedding
%A Fatemeh Farhadi
%A Allahbakhsh, Mohammad
%A Ali Maghsoudi
%A Armin, Nadia
%A Amintoosi, Haleh
%J Briefings in Bioinformatics
%@ 1467-5463
%D 2023