一种基于深度变分门神经网络的疾病小核糖核酸关联预测模型

郭延哺; 马欢; 李朝阳; 周冬明

doi:10.11999/JEIT220354

一种基于深度变分门神经网络的疾病小核糖核酸关联预测模型

doi: 10.11999/JEIT220354

1.
郑州轻工业大学软件学院郑州 450002
2.
云南大学信息学院昆明 650500

基金项目: 国家自然科学基金(62066047)，郑州轻工业大学博士科研基金(2021BSJJ032)

详细信息

作者简介:
郭延哺：男，博士，讲师，研究方向为神经网络理论与应用、生物信息计算

马欢：男，硕士，副教授，研究方向为机器学习和智能信息处理

李朝阳：男，博士，讲师，研究方向为机器学习和信息安全

周冬明：男，博士，教授，研究方向为神经网络理论与应用、生物医学工程

通讯作者:
周冬明　zhoudm@ynu.edu.cn

中图分类号: TN911.7; TP391
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- 被引次数: 0
出版历程
- 收稿日期: 2022-03-31
- 修回日期: 2022-06-19
- 网络出版日期: 2022-06-24
- 刊出日期: 2023-05-10

Deep Disease MicroRNA Association Prediction via Variational Gated Graph Autoencoders

1.
College of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China
2.
School of Information Science and Engineering, Yunnan University, Kunming 650500, China

Funds: The National Natural Science Foundation of China (62066047), The Doctor Scientific Research Fund of Zhengzhou University of Light Industry (2021BSJJ032)

摘要

摘要: 小核糖核酸(miRNA)在基因表达和转录等过程中具有重要作用，与疾病的产生有着密切关联。对于疾病miRNA关联识别，生物鉴定方法代价高、耗时长和效率低。为快速自适应提取疾病和miRNA构成的异质网络信息，该文基于通道型注意力设计变分门图自编码器和门多层感知器，构建一种深度变分门神经网络模型(VGAE-N)并用于疾病miRNA关联预测任务。该模型整合miRNA及疾病的多种相似度信息得到miRNA和疾病的整合相似性特征，然后基于多数据融合的整合相似性网络和疾病miRNA邻接信息，利用变分门图自编码器提取miRNA和疾病网络的拓扑信息和语义信息；其次基于疾病miRNA关联矩阵，利用非负矩阵分解提取miRNA和疾病的低维线性去噪特征；最后，利用门多层感知器融合miRNA和疾病特征，预测其关联关系。实验结果表明VGAE-N模型能更有效地预测疾病miRNA关联，可为生物实验提供可靠的技术支撑。
- 变分自编码器 /
- 矩阵分解 /
- 门机制 /
- 疾病小核糖核酸关联预测
Abstract: micro RiboNucleic Acid (miRNA) plays an important role in the process of gene expression and transcription, and is closely related to the production of diseases. Biological experimental methods for disease miRNA association prediction are costly and time-consuming. To extract contextual information in heterogeneous networks of diseases and miRNAs, a high-performance Variational Gated AutoEncoder Network (VGAE-N) and a gated multi-layer perceptron are designed based on gated mechanisms and convolutions, and then a deep variational gated neural model is constructed for inferring disease miRNA associations. Multisource information first is integrated between miRNAs and diseases, and then the comprehensive similarity matrix is obtained between miRNAs and diseases. Based on a comprehensive network and the miRNA disease adjacency matrix, topological information is further extracted for miRNAs and diseases, respectively. Based on the miRNA disease adjacency matrix, the nonnegative matrix decomposition is used to extract low dimensional denoising features of miRNA and diseases. The experimental results show that the proposed model can effectively conduct miRNA disease association prediction, and provide reliable technical support for biological experiments.
- Variational autoencoder /
- Matrix factorization /
- Gated mechanisms /
- Micro (miRNA) disease associations

HTML全文

图 1 基于多源数据融合的深度变分门神经网络模型

下载: 全尺寸图片幻灯片

图 2 变分门图自编码器

下载: 全尺寸图片幻灯片

图 3 不同学习率对模型VGAE-N性能的影响

下载: 全尺寸图片幻灯片

图 4 不同维度的特征表示对模型VGAE-N性能的影响

下载: 全尺寸图片幻灯片

表 1 VGAE-N模型的消融实验研究(%)

模型	查准率	查全率	F1分数	AUROC	AUPRC
VGAE-N	90.79	91.16	90.97	96.68	96.53
-BN	90.42	90.11	90.25	96.35	96.24
-FF	89.78	91.00	90.38	96.49	96.33
-GM	89.62	92.15	90.86	96.61	96.42

下载: 导出CSV

表 2 在HMDDv3.2数据集上VGAE-N和基准模型的性能(%)

模型		查准率	查全率	F1分数	AUROC	AUPRC
基准模型	MRSLA^[31]	84.42	84.85	84.63	90.29	89.53
	ABMDA^[32]	85.13	83.96	84.54	91.87	90.47
	VAEMDA^[33]	84.84	85.96	85.39	92.38	91.57
	DBN-MF ^[34]	86.42	86.13	86.27	93.51	92.33
	VGAMF^[20]	87.59	86.69	87.14	94.70	94.03
本文工作	VGAE-N	90.79	91.16	90.97	96.68	96.53

下载: 导出CSV

表 3 在HMDDv2.0数据集上VGAE-N和基准模型的性能(%)

模型		查准率	查全率	F1分数	AUROC	AUPRC
基准模型	RW^[6]	71.12	86.15	77.94	75.84	78.96
	MDA-CNN^[15]	82.44	80.56	81.44	88.97	88.87
	VGAE-MDA^[19]	85.76	87.62	86.68	93.94	93.90
	DBN-MF^[34]	83.77	85.26	84.51	91.69	90.43
	VGAMF^[20]	85.23	85.50	85.36	92.80	92.25
本文工作	VGAE-N	89.58	89.09	89.32	95.38	95.21

下载: 导出CSV

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