AC-Caps: Attention Based Capsule Network for Predicting RBP Binding Sites of LncRNA

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ORIGINAL RESEARCH ARTICLE

AC‑Caps: Attention Based Capsule Network for Predicting RBP Binding Sites of LncRNA Jinmiao Song1,2 · Shengwei Tian3 · Long Yu4 · Yan Xing5 · Qimeng Yang1 · Xiaodong Duan2 · Qiguo Dai2 Received: 11 January 2020 / Revised: 18 May 2020 / Accepted: 30 May 2020 © International Association of Scientists in the Interdisciplinary Areas 2020

Abstract Long non-coding RNA(lncRNA) is one of the non-coding RNAs longer than 200 nucleotides and it has no protein encoding function. LncRNA plays a key role in many biological processes. Studying the RNA-binding protein (RBP) binding sites on the lncRNA chain helps to reveal epigenetic and post-transcriptional mechanisms, to explore the physiological and pathological processes of cancer, and to discover new therapeutic breakthroughs. To improve the recognition rate of RBP binding sites and reduce the experimental time and cost, many calculation methods based on domain knowledge to predict RBP binding sites have emerged. However, these prediction methods are independent of nucleotides and do not take into account nucleotide statistics. In this paper, we use a high-order statistical-based encoding scheme, then the encoded lncRNA sequences are fed into a hybrid deep learning architecture named AC-Caps. It consists of a joint processing layer(composed of attention mechanism and convolutional neural network) and a capsule network. The AC-Caps model was evaluated using 31 independent experimental data sets from 12 lncRNA-binding proteins. In experiments, our method achieves excellent performance, with an average area under the curve (AUC) of 0.967 and an average accuracy (ACC) of 92.5%, which are 0.014, 2.3%, 0.261, 28.9%, 0.189, and 21.8% higher than HOCCNNLB, iDeepS, and DeepBind, respectively. The results show that the AC-Caps method can reliably process the large-scale RBP binding site data on the lncRNA chain, and the prediction performance is better than existing deep-learning models. The source code of AC-Caps and the datasets used in this paper are available at https​://githu​b.com/Jinmi​aoS/AC-Caps. Keywords  Attention mechanism · Capsule network · Convolutional neural network · lncRNA-binding protein

1 Introduction Electronic supplementary material  The online version of this article (https​://doi.org/10.1007/s1253​9-020-00379​-3) contains supplementary material, which is available to authorized users. * Shengwei Tian [email protected] * Yan Xing [email protected] 1



School of Information Science and Engineering, Xinjiang University, Urumqi 830008, China

2



Dalian Key Lab of Digital Technology for National Culture, Dalian Minzu University, Dalian 116600, China

3

School of Software, Xinjiang University, Urumqi 830046, China

4

Network Center, Xinjiang University, Urumqi 830046, China

5

Imaging Center, Xinjiang Medical University Affiliated First Hospital, Urumqi 830011, China



RNA-binding proteins (RBPs) in cells interacts with specific RNA to form the ribonucleoprotein (RNP) complex, which play an important role in genome stabilit