Open Access

ARTICLE

Tissue specific prediction of N⁶-methyladenine sites based on an ensemble of multi-input hybrid neural network

CANGZHI JIA¹, DONG JIN¹, XIN WANG¹, QI ZHAO^2,*

1 School of Science, Dalian Maritime University, Dalian, 116026, China
2 School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China

* Corresponding Author:QI ZHAO. Email:

BIOCELL 2022, 46(4), 1105-1121. https://doi.org/10.32604/biocell.2022.016655

Received 12 April 2021; Accepted 02 July 2021; Issue published 15 December 2021

Abstract

N⁶-Methyladenine is a dynamic and reversible post translational modification, which plays an essential role in various biological processes. Because of the current inability to identify m6A-containing mRNAs, computational approaches have been developed to identify m6A sites in DNA sequences. Aiming to improve prediction performance, we introduced a novel ensemble computational approach based on three hybrid deep neural networks, including a convolutional neural network, a capsule network, and a bidirectional gated recurrent unit (BiGRU) with the self-attention mechanism, to identify m6A sites in four tissues of three species. Across a total of 11 datasets, we selected different feature subsets, after optimized from 4933 dimensional features, as input for the deep hybrid neural networks. In addition, to solve the deviation caused by the relatively small number of experimentally verified samples, we constructed an ensemble model through integrating five sub-classifiers based on different training datasets. When compared through 5-fold cross-validation and independent tests, our model showed its superiority to previous methods, im6A-TS-CNN and iRNA-m6A.

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