McBel-Plnc:蛋白质- lncrna相互作用多类多标签分类的深度学习模型

Natsuda Navamajiti, Thammakorn Saethang, D. Wichadakul
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引用次数: 4

摘要

长链非编码rna (lncRNAs)的一个主要功能是作为一个支架,促进多种蛋白质形成复合物。然而,大多数现有的蛋白质- rna相互作用预测模型都是作为二分类器提出的,其局限于预测非编码rna与每个rna结合蛋白(RBP)之间的相互作用。因此,为了预测lncRNA是否起到支架的作用,我们将这个问题视为一个多类别多标签分类问题。为了解决这个问题,我们从POSTAR2数据库中选择了高可信度的CLIP-seq数据,并增加了具有少量相互作用lncrna的RBP类的数据。然后,我们基于卷积神经网络(CNN)和长短期记忆(LSTM),使用从准备数据随机生成的五个数据集中的每一个,构建了一个用于多类多标签分类的深度学习模型,称为McBel-Plnc。在宏观平均的基础上,5个模型的平均精度为0.9151±0.0038,召回率为0.5786±0.0208。小的标准差证实了模型的稳定性。与二值相关方法的iDeepE相比,iDeepE的查全率更高,查准率显著低于前者(分别为0.6912和0.1987)。这一结果表明,我们的模型能够预测蛋白质与lncrna的相互作用,特别是与多种蛋白质靶向的lncrna的相互作用。这表明有可能推断lncRNA的功能和分子机制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
McBel-Plnc: A Deep Learning Model for Multiclass Multilabel Classification of Protein-lncRNA Interactions
One main function of long non-coding RNAs (lncRNAs) is to act as a scaffold facilitating multiple proteins to form complexes. Most of available prediction models for protein-RNA interactions, however, were proposed as a binary classifier, which limited on predicting the interaction between the non-coding RNAs and each individual RNA-binding protein (RBP). Hence, to predict if a lncRNA is acting as a scaffold, we consider this problem as a multiclass multilabel classification problem. To solve this problem, the high confident CLIP-seq data were selected from the POSTAR2 database with an augmentation of the data for the RBP classes with a small number of interacting lncRNAs. We then constructed a deep learning model for multiclass multilabel classification, called McBel-Plnc, based on the convolutional neural network (CNN) and long-short term memory (LSTM) using each of the five datasets randomly generated from the prepared data. Based on macro average, the test results showed the high precision of 0.9151 ± 0.0038 averaged from the five models with the lower recall of 0.5786 ± 0.0208. The small standard deviations confirmed the model stability. Comparing with iDeepE with a binary relevance method, iDeepE got the higher recall with the significantly lower precision (0.6912 and 0.1987, respectively). This result suggested that our model is competent to predict the protein-lncRNA interactions, especially with the lncRNAs targeted by multiple proteins. This suggested the potential to infer the insights of lncRNA functions and molecular mechanisms.
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