Identification of Functional piRNAs Using a Convolutional Neural Network

Syed Danish Ali; Waleed Alam; Hilal TayaraKil To Chong

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Identification of Functional piRNAs Using a Convolutional Neural Network

机译：Identification of Functional piRNAs Using a Convolutional Neural Network

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Piwi-interacting RNAs (piRNAs) are a distinct sub-class of small non-coding RNAs that are mainly responsible for germline stem cell maintenance, gene stability, and maintaining genome integrity by repression of transposable elements. piRNAs are also expressed aberrantly and associated with various kinds of cancers. To identify piRNAs and their role in guiding target mRNA deadenylation, the currently available computational methods require urgent improvements in performance. To facilitate this, we propose a robust predictor based on a lightweight and simplified deep learning architecture using a convolutional neural network (CNN) to extract significant features from raw RNA sequences without the need for more customized features. The proposed model’s performance is comprehensively evaluated using $k$k-fold cross-validation on a benchmark dataset. The proposed model significantly outperforms existing computational methods in the prediction of piRNAs and their role in target mRNA deadenylation. In addition, a user-friendly and publicly-accessible web server is available at http://nsclbio.jbnu.ac.kr/tools/2S-piRCNN/.

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《IEEE/ACM transactions on computational biology and bioinformatics》 |2022年第3期|1661-1669|共9页
作者
Syed Danish Ali; Waleed Alam; Hilal TayaraKil To Chong;
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作者单位

Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, South Korea;

School of International Engineering and Science, Jeonbuk National University, Jeonju, South Korea;

Advanced Electronics and Information Research Center, Jeonbuk National University, Jeonju, South Korea;

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正文语种英语
中图分类生物信息论;
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Computational modeling; RNA; Feature extraction; Benchmark testing; Biological system modeling; Bioinformatics; Mathematical model;

Identification of Functional piRNAs Using a Convolutional Neural Network

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