预测mirna与疾病关联的自适应深度传播图神经网络。

IF 2.5 3区 生物学 Q3 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Hua Hu, Huan Zhao, Tangbo Zhong, Xishang Dong, Lei Wang, Pengyong Han, Zhengwei Li
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引用次数: 1

摘要

背景:大量实验表明,miRNA的异常表达与疾病的发生、诊断和治疗密切相关。确定mirna与疾病之间的关联对于复杂人类疾病的临床应用具有重要意义。然而,传统的生物学实验方法和基于计算的方法存在许多局限性,这促使人们开发更高效、更准确的深度学习方法来预测mirna与疾病的关联。结果:本文提出了一种基于自适应深度传播图神经网络的mirna -疾病关联预测模型(ADPMDA)。我们首先基于已知的miRNA-疾病对、miRNA综合相似度信息、miRNA序列信息和疾病相似度信息构建了miRNA-疾病异质性图。然后,我们将mirna和疾病的特征投射到一个低维空间中。然后利用注意机制对中心节点的局部特征进行聚合。其中,采用自适应深度传播图神经网络学习节点嵌入,可以自适应调整节点的局部和全局信息。最后,利用多层感知器对mirna -疾病对进行评分。结论:在人类microRNA疾病数据库v3.0数据集上的实验表明,在5倍交叉验证下,ADPMDA的平均AUC值达到94.75%。我们进一步对食管肿瘤、肺肿瘤和淋巴瘤进行了病例研究,以证实我们提出的模型的有效性,并分别确认了与这些疾病相关的前50个预测mirna中的49个、49个、47个。这些结果证明了我们的模型在预测mirna -疾病关联方面的有效性和优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive deep propagation graph neural network for predicting miRNA-disease associations.

Background: A large number of experiments show that the abnormal expression of miRNA is closely related to the occurrence, diagnosis and treatment of diseases. Identifying associations between miRNAs and diseases is important for clinical applications of complex human diseases. However, traditional biological experimental methods and calculation-based methods have many limitations, which lead to the development of more efficient and accurate deep learning methods for predicting miRNA-disease associations.

Results: In this paper, we propose a novel model on the basis of adaptive deep propagation graph neural network to predict miRNA-disease associations (ADPMDA). We first construct the miRNA-disease heterogeneous graph based on known miRNA-disease pairs, miRNA integrated similarity information, miRNA sequence information and disease similarity information. Then, we project the features of miRNAs and diseases into a low-dimensional space. After that, attention mechanism is utilized to aggregate the local features of central nodes. In particular, an adaptive deep propagation graph neural network is employed to learn the embedding of nodes, which can adaptively adjust the local and global information of nodes. Finally, the multi-layer perceptron is leveraged to score miRNA-disease pairs.

Conclusion: Experiments on human microRNA disease database v3.0 dataset show that ADPMDA achieves the mean AUC value of 94.75% under 5-fold cross-validation. We further conduct case studies on the esophageal neoplasm, lung neoplasms and lymphoma to confirm the effectiveness of our proposed model, and 49, 49, 47 of the top 50 predicted miRNAs associated with these diseases are confirmed, respectively. These results demonstrate the effectiveness and superiority of our model in predicting miRNA-disease associations.

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来源期刊
Briefings in Functional Genomics
Briefings in Functional Genomics BIOTECHNOLOGY & APPLIED MICROBIOLOGY-GENETICS & HEREDITY
CiteScore
6.30
自引率
2.50%
发文量
37
审稿时长
6-12 weeks
期刊介绍: Briefings in Functional Genomics publishes high quality peer reviewed articles that focus on the use, development or exploitation of genomic approaches, and their application to all areas of biological research. As well as exploring thematic areas where these techniques and protocols are being used, articles review the impact that these approaches have had, or are likely to have, on their field. Subjects covered by the Journal include but are not restricted to: the identification and functional characterisation of coding and non-coding features in genomes, microarray technologies, gene expression profiling, next generation sequencing, pharmacogenomics, phenomics, SNP technologies, transgenic systems, mutation screens and genotyping. Articles range in scope and depth from the introductory level to specific details of protocols and analyses, encompassing bacterial, fungal, plant, animal and human data. The editorial board welcome the submission of review articles for publication. Essential criteria for the publication of papers is that they do not contain primary data, and that they are high quality, clearly written review articles which provide a balanced, highly informative and up to date perspective to researchers in the field of functional genomics.
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