Apr 12, 2020

miTAR: a hybrid deep learning-based approach for predicting miRNA targets

BioRxiv : the Preprint Server for Biology
Tongjun GuJ.-H. Lee

Abstract

microRNAs (miRNAs) are a major type of small RNA that alter gene expression at the post-transcriptional or translational level. They have been shown to play important roles in a wide range of biological processes. Many computational methods have been developed to predict targets of miRNAs in order to understand miRNAs' function. However, the majority of the methods depend on a set of pre-defined features that require considerable effort and resources to compute, and these methods often do not effectively on the prediction of miRNA targets. Therefore, we developed a novel hybrid deep learning-based approach that is capable to predict miRNA targets at a higher accuracy. Our approach integrates two deep learning methods: convolutional neural networks (CNNs) that excel in learning spatial features, and recurrent neural networks (RNNs) that discern sequential features. By combining CNNs and RNNs, our approach has the advantages of learning both the intrinsic spatial and sequential features of miRNA:target. The inputs for the approach are raw sequences of miRNA and gene sequences. Data from two latest miRNA target prediction studies were used in our study: the DeepMirTar dataset and the miRAW dataset. Two models were obtained by trai...Continue Reading

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Mentioned in this Paper

Benign Neoplasm of Testis
Genes
RNA, Untranslated
Trees (plant)
Neoplasm of Uncertain or Unknown Behavior of Testis
Maintenance of Meristem Identity
Gene Expression
Neural Stem Cells
Cell Differentiation Process
Pan troglodytes

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