当前位置: X-MOL 学术arXiv.cs.AI › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Graph Convolution Networks Using Message Passing and Multi-Source Similarity Features for Predicting circRNA-Disease Association
arXiv - CS - Artificial Intelligence Pub Date : 2020-09-15 , DOI: arxiv-2009.07173
Thosini Bamunu Mudiyanselage, Xiujuan Lei, Nipuna Senanayake, Yanqing Zhang, Yi Pan

Graphs can be used to effectively represent complex data structures. Learning these irregular data in graphs is challenging and still suffers from shallow learning. Applying deep learning on graphs has recently showed good performance in many applications in social analysis, bioinformatics etc. A message passing graph convolution network is such a powerful method which has expressive power to learn graph structures. Meanwhile, circRNA is a type of non-coding RNA which plays a critical role in human diseases. Identifying the associations between circRNAs and diseases is important to diagnosis and treatment of complex diseases. However, there are limited number of known associations between them and conducting biological experiments to identify new associations is time consuming and expensive. As a result, there is a need of building efficient and feasible computation methods to predict potential circRNA-disease associations. In this paper, we propose a novel graph convolution network framework to learn features from a graph built with multi-source similarity information to predict circRNA-disease associations. First we use multi-source information of circRNA similarity, disease and circRNA Gaussian Interaction Profile (GIP) kernel similarity to extract the features using first graph convolution. Then we predict disease associations for each circRNA with second graph convolution. Proposed framework with five-fold cross validation on various experiments shows promising results in predicting circRNA-disease association and outperforms other existing methods.

中文翻译:

图卷积网络使用消息传递和多源相似性特征预测circRNA-疾病关联

图形可以用来有效地表示复杂的数据结构。在图形中学习这些不规则数据具有挑战性,但仍然遭受浅层学习的困扰。对图进行深度学习最近在社会分析,生物信息学等许多应用中显示出良好的性能。消息传递图卷积网络是一种强大的方法,具有表达图结构的能力。同时,circRNA是一种非编码RNA,在人类疾病中起着至关重要的作用。鉴定circRNA与疾病之间的关联对于复杂疾病的诊断和治疗很重要。但是,它们之间的已知关联数量有限,进行生物学实验以鉴定新的关联既费时又昂贵。结果是,需要建立有效且可行的计算方法来预测潜在的circRNA-疾病关联。在本文中,我们提出了一种新颖的图卷积网络框架,以从使用多源相似性信息构建的图中学习特征以预测circRNA-疾病关联。首先,我们使用circRNA相似性,疾病和circRNA高斯相互作用谱(GIP)内核相似性的多源信息,使用第一个图卷积提取特征。然后,我们用第二个图卷积预测每个circRNA的疾病关联。在各种实验中具有五倍交叉验证的拟议框架在预测circRNA-疾病关联方面显示出令人鼓舞的结果,并且优于其他现有方法。我们提出了一种新颖的图卷积网络框架,以从使用多源相似性信息构建的图中学习特征来预测circRNA-疾病关联。首先,我们使用circRNA相似性,疾病和circRNA高斯相互作用谱(GIP)内核相似性的多源信息,使用第一个图卷积提取特征。然后,我们用第二个图卷积预测每个circRNA的疾病关联。在各种实验中具有五倍交叉验证的拟议框架在预测circRNA-疾病关联方面显示出令人鼓舞的结果,并且优于其他现有方法。我们提出了一种新颖的图卷积网络框架,以从使用多源相似性信息构建的图中学习特征来预测circRNA-疾病关联。首先,我们使用circRNA相似性,疾病和circRNA高斯相互作用谱(GIP)内核相似性的多源信息,使用第一个图卷积提取特征。然后,我们用第二个图卷积预测每个circRNA的疾病关联。在各种实验中具有五倍交叉验证的拟议框架在预测circRNA-疾病关联方面显示出令人鼓舞的结果,并且优于其他现有方法。疾病和circRNA高斯相互作用谱(GIP)内核相似性,以便使用第一个图卷积提取特征。然后,我们用第二个图卷积预测每个circRNA的疾病关联。在各种实验中具有五倍交叉验证的拟议框架在预测circRNA-疾病关联方面显示出令人鼓舞的结果,并且优于其他现有方法。疾病和circRNA高斯相互作用谱(GIP)内核相似性,以便使用第一个图卷积提取特征。然后,我们用第二个图卷积预测每个circRNA的疾病关联。在各种实验中具有五倍交叉验证的拟议框架在预测circRNA-疾病关联方面显示出令人鼓舞的结果,并且优于其他现有方法。
更新日期:2020-09-16
down
wechat
bug