Neural inductive matrix completion with graph convolutional networks for miRNA-disease association prediction
Neural inductive matrix completion with graph convolutional networks for miRNA-disease association prediction
复制标题
使用图卷积网络完成神经归纳矩阵，用于 miRNA 疾病关联预测
DOI：
10.1093/bioinformatics/btz965
复制
发表时间：
2020-04-15
期刊：
影响因子：
5.8
通讯作者：
Zhou, Wei
中科院分区：
文献类型：
--
作者：
Li, Jin;Zhang, Sai;Zhou, Wei
动机：预测microRNAs(MiRNAs)与疾病之间的联系在识别与人类疾病相关的miRNAs方面发挥着重要作用。由于通过生物学实验鉴定miRNA与疾病的关联既耗时又昂贵，计算方法目前被用作确定疾病与miRNA之间潜在关联的有效补充。结果：我们提出了一种新的神经诱导矩阵补全图卷积网络(NIMCGCN)方法来预测miRNA与疾病的关联。NIMCGCN首先使用图卷积网络从miRNA和疾病相似性网络中学习miRNA和疾病潜在特征表示。然后，将学习到的特征输入到一种新的神经归纳矩阵完成(NIMC)模型中，生成关联矩阵完成。NIMCGCN的参数是基于已知的miRNA-疾病关联数据以端到端的监督方式学习的。我们将提出的方法与其他最先进的方法进行了比较。接收器工作特性曲线下面积结果表明，我们的方法明显优于现有方法。此外，针对结肠癌、淋巴瘤和肾癌这三种人类高危疾病预测的前50个miRNAs中，有50个、47个和48个得到了实验文献的验证。最后，当以乳腺癌为例评估NIMCGCN在没有任何已知相关miRNAs的情况下预测新疾病的能力时，预测准确率达到100%。
Motivation: Predicting the association between microRNAs (miRNAs) and diseases plays an import role in identifying human disease-related miRNAs. As identification of miRNA-disease associations via biological experiments is time-consuming and expensive, computational methods are currently used as effective complements to determine the potential associations between disease and miRNA.Results: We present a novel method of neural inductive matrix completion with graph convolutional network (NIMCGCN) for predicting miRNA-disease association. NIMCGCN first uses graph convolutional networks to learn miRNA and disease latent feature representations from the miRNA and disease similarity networks. Then, learned features were input into a novel neural inductive matrix completion (NIMC) model to generate an association matrix completion. The parameters of NIMCGCN were learned based on the known miRNA-disease association data in a supervised end-to-end way. We compared the proposed method with other state-of-the-art methods. The area under the receiver operating characteristic curve results showed that our method is significantly superior to existing methods. Furthermore, 50, 47 and 48 of the top 50 predicted miRNAs for three high-risk human diseases, namely, colon cancer, lymphoma and kidney cancer, were verified using experimental literature. Finally, 100% prediction accuracy was achieved when breast cancer was used as a case study to evaluate the ability of NIMCGCN for predicting a new disease without any known related miRNAs.