ESGC-MDA: Identifying miRNA-disease associations using enhanced Simple Graph Convolutional Networks.

IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Xuehua Bi, Chunyang Jiang, Cheng Yan, Kai Zhao, Linlin Zhang, Jianxin Wang
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引用次数: 0

Abstract

MiRNAs play an important role in the occurrence and development of human disease. Identifying potential miRNA-disease associations is valuable for disease diagnosis and treatment. Therefore, it is urgent to develop efficient computational methods for predicting potential miRNA-disease associations to reduce the cost and time associated with biological wet experiments. In addition, high-quality feature representation remains a challenge for miRNA-disease association prediction using graph neural network methods. In this paper, we propose a method named ESGC-MDA, which employs an enhanced Simple Graph Convolution Network to identify miRNA-disease associations. We first construct a bipartite attributed graph for miRNAs and diseases by computing multi-source similarity. Then, we enhance the feature representations of miRNA and disease nodes by applying two strategies in the simple convolution network, which include randomly dropping messages during propagation to ensure the model learns more reliable feature representations, and using adaptive weighting to aggregate features from different layers. Finally, we calculate the prediction scores of miRNA-disease pairs by using a fully connected neural network decoder. We conduct 5-fold cross-validation and 10-fold cross-validation on HDMM v2.0 and HMDD v3.2, respectively, and ESGC-MDA achieves better performance than state-of-the-art baseline methods. The case studies for cardiovascular disease, lung cancer and colon cancer also further confirm the effectiveness of ESGC-MDA. The source codes are available at https://github.com/bixuehua/ESGC-MDA.

ESGC-MDA:利用增强型简单图卷积网络识别 miRNA 与疾病的关联。
miRNA 在人类疾病的发生和发展中发挥着重要作用。识别潜在的 miRNA 与疾病的关联对疾病诊断和治疗非常有价值。因此,当务之急是开发预测潜在 miRNA 与疾病关联的高效计算方法,以减少生物湿实验的成本和时间。此外,高质量的特征表示仍然是使用图神经网络方法预测 miRNA-疾病关联的一个挑战。本文提出了一种名为 ESGC-MDA 的方法,它采用增强型简单图卷积网络来识别 miRNA 与疾病的关联。我们首先通过计算多源相似性为 miRNA 和疾病构建一个双方属性图。然后,我们通过在简单卷积网络中应用两种策略来增强 miRNA 和疾病节点的特征表示,包括在传播过程中随机丢弃信息以确保模型学习到更可靠的特征表示,以及使用自适应加权来聚合不同层的特征。最后,我们使用全连接神经网络解码器计算 miRNA 疾病对的预测得分。我们分别在 HDMM v2.0 和 HMDD v3.2 上进行了 5 倍交叉验证和 10 倍交叉验证,ESGC-MDA 比最先进的基线方法取得了更好的性能。对心血管疾病、肺癌和结肠癌的案例研究也进一步证实了 ESGC-MDA 的有效性。源代码见 https://github.com/bixuehua/ESGC-MDA。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.50
自引率
6.70%
发文量
479
审稿时长
3 months
期刊介绍: IEEE/ACM Transactions on Computational Biology and Bioinformatics emphasizes the algorithmic, mathematical, statistical and computational methods that are central in bioinformatics and computational biology; the development and testing of effective computer programs in bioinformatics; the development of biological databases; and important biological results that are obtained from the use of these methods, programs and databases; the emerging field of Systems Biology, where many forms of data are used to create a computer-based model of a complex biological system
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