AC-Caps: Attention Based Capsule Network for Predicting RBP Binding Sites of LncRNA
AC-Caps: Attention Based Capsule Network for Predicting RBP Binding Sites of LncRNA
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DOI：
10.1007/s12539-020-00379-3
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发表时间：
2020-06
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通讯作者：
Jinmiao Song;Shengwei Tian;Long Yu;Yan Xing;Qimeng Yang;Xiaodong Duan;Qiguo Dai
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作者：
Jinmiao Song;Shengwei Tian;Long Yu;Yan Xing;Qimeng Yang;Xiaodong Duan;Qiguo Dai
长非编码RNA(Long Non-Coding RNA，LncRNA)是一种长度超过200个核苷酸的非编码RNA，不具有蛋白质编码功能。LncRNA在许多生物过程中起着关键作用。研究lncRNA链上的RNA结合蛋白(RBP)结合位点有助于揭示表观遗传和转录后机制，探索癌症的生理和病理过程，并发现新的治疗突破。为了提高RBP结合位点的识别率，减少实验时间和成本，出现了许多基于领域知识预测RBP结合位点的计算方法。然而，这些预测方法与核苷酸无关，也没有考虑核苷酸统计。在本文中，我们使用了一种基于高阶统计的编码方案，然后将编码后的lncRNA序列馈送到名为AC-Caps的混合深度学习结构中。它由联合处理层(由注意机制和卷积神经网络组成)和胶囊网络组成。AC-Caps模型使用来自12个LncRNA结合蛋白的31个独立的实验数据集进行了评估。在实验中，我们的方法取得了良好的性能，平均曲线下面积为0.967，平均准确率为92.5%，分别比HOCCNNLB、iDeepS和DeepBind分别提高了0.014、2.3%、0.261、28.9%、0.189和21.8%。结果表明，AC-Caps方法能够可靠地处理大规模的LncRNA链上的RBP结合位点数据，其预测性能优于现有的深度学习模型。AC-CAPS的源代码和本文中使用的数据集可以在https://github.com/JinmiaoS/AC-Caps.上获得
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.