论文标题
LNCRNA-疾病协会协会预测的基于信念网络的深度代表性学习
Deep Belief Network based representation learning for lncRNA-disease association prediction
论文作者
论文摘要
背景：长期非编码RNA（LNCRNA）领域的扩展研究表明，LNCRNA在许多复杂疾病中的表达异常。准确地识别lncRNA-疾病的关联对于理解LNCRNA功能和疾病机制至关重要。 LNCRNA-DISESE关联的预测涉及许多机器学习技术，这些技术使用不同的生物相互作用网络和相关特征。从网络结构化数据中学习的特征学习是基于机器学习方法的限制因素之一。基于图形神经网络的技术通过无监督的特征学习解决了这一限制。深信网络（DBN）最近用于生物网络分析中，以了解网络特征的潜在表示。 方法：在本文中，我们提出了一种基于DBN的LNCRNA-疾病关联模型（DBNLDA），来自LNCRNA，疾病和miRNA相互作用。该体系结构包含三个主要的模块网络结构，基于DBN的功能学习和基于神经网络的预测。首先，我们构建了三个异质网络，例如lncrna-miRNA相似性（LMS），疾病 - miRNA相似性（DMS）和LNCRNA-疾病酶协会（LDA）网络。从相似性网络的节点嵌入矩阵中，通过两个基于DBN的子网单独学习lncRNA-酶表示。第三个DBN从提到的两个子网的输出中学到了LNCRNA-DISESE的联合表示。该联合特征表示由ANN分类器预测关联评分。 结果：针对最先进方法使用的标准数据集进行测试时，提出的方法获得的AUC为0.96，AUPR为0.967。对乳腺癌，肺癌和胃癌病例的分析也肯定了DBNLDA在预测明显的lncRNA-蛋白酶关联方面的有效性。
Background: The expanding research in the field of long non-coding RNAs(lncRNAs) showed abnormal expression of lncRNAs in many complex diseases. Accurately identifying lncRNA-disease association is essential in understanding lncRNA functionality and disease mechanism. There are many machine learning techniques involved in the prediction of lncRNA-disease association which use different biological interaction networks and associated features. Feature learning from the network structured data is one of the limiting factors of machine learning-based methods. Graph neural network based techniques solve this limitation by unsupervised feature learning. Deep belief networks (DBN) are recently used in biological network analysis to learn the latent representations of network features. Method: In this paper, we propose a DBN based lncRNA-disease association prediction model (DBNLDA) from lncRNA, disease and miRNA interactions. The architecture contains three major modules-network construction, DBN based feature learning and neural network-based prediction. First, we constructed three heterogeneous networks such as lncRNA-miRNA similarity (LMS), disease-miRNA similarity (DMS) and lncRNA-disease association (LDA) network. From the node embedding matrices of similarity networks, lncRNA-disease representations were learned separately by two DBN based subnetworks. The joint representation of lncRNA-disease was learned by a third DBN from outputs of the two subnetworks mentioned. This joint feature representation was used to predict the association score by an ANN classifier. Result: The proposed method obtained AUC of 0.96 and AUPR of 0.967 when tested against standard dataset used by the state-of-the-art methods. Analysis on breast, lung and stomach cancer cases also affirmed the effectiveness of DBNLDA in predicting significant lncRNA-disease associations.
