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

• Published in: Interdisciplinary sciences : computational life sciences 2020-12, Vol.12 (4), p.414-423
• Main Authors: Song, Jinmiao, Tian, Shengwei, Yu, Long, Xing, Yan, Yang, Qimeng, Duan, Xiaodong, Dai, Qiguo
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Binding sites; Biological activity; Biomedical and Life Sciences; Chains; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Appl. in Life Sciences; Epigenetics; Health Sciences; Life Sciences; Mathematical and Computational Physics; Medicine; Neural networks; Non-coding RNA; Nucleotides; Original Research Article; Post-transcription; Proteins; RNA-binding protein; Sequences; Source code; Statistics for Life Sciences; Theoretical; Theoretical and Computational Chemistry
• ISSN: 1913-2751; 1867-1462
• DOI: 10.1007/s12539-020-00379-3

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://github.com/JinmiaoS/AC-Caps .