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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

郭延哺, 马欢, 李朝阳, 周冬明. 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[J]. 电子与信息学报, 2023, 45(5): 1786-1794. doi: 10.11999/JEIT220354
引用本文: 郭延哺, 马欢, 李朝阳, 周冬明. 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[J]. 电子与信息学报, 2023, 45(5): 1786-1794. doi: 10.11999/JEIT220354
GUO Yanbu, MA Huan, LI Chaoyang, ZHOU Dongming. Deep Disease MicroRNA Association Prediction via Variational Gated Graph Autoencoders[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1786-1794. doi: 10.11999/JEIT220354
Citation: GUO Yanbu, MA Huan, LI Chaoyang, ZHOU Dongming. Deep Disease MicroRNA Association Prediction via Variational Gated Graph Autoencoders[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1786-1794. 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
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

Deep Disease MicroRNA Association Prediction via Variational Gated Graph Autoencoders

Funds: The National Natural Science Foundation of China (62066047), The Doctor Scientific Research Fund of Zhengzhou University of Light Industry (2021BSJJ032)
  • 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。
  • 图  1  基于多源数据融合的深度变分门神经网络模型

    图  2  变分门图自编码器

    图  3  不同学习率对模型VGAE-N性能的影响

    图  4  不同维度的特征表示对模型VGAE-N性能的影响

    表  1  VGAE-N模型的消融实验研究(%)

    模型查准率查全率F1分数AUROCAUPRC
    VGAE-N90.7991.1690.9796.6896.53
    -BN90.4290.1190.2596.3596.24
    -FF89.7891.0090.3896.4996.33
    -GM89.6292.1590.8696.6196.42
    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AUROCAUPRC
    基准模型MRSLA[31]84.4284.8584.6390.2989.53
    ABMDA[32]85.1383.9684.5491.8790.47
    VAEMDA[33]84.8485.9685.3992.3891.57
    DBN-MF [34]86.4286.1386.2793.5192.33
    VGAMF[20]87.5986.6987.1494.7094.03
    本文工作VGAE-N90.7991.1690.9796.6896.53
    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AUROCAUPRC
    基准模型RW[6]71.1286.1577.9475.8478.96
    MDA-CNN[15]82.4480.5681.4488.9788.87
    VGAE-MDA[19]85.7687.6286.6893.9493.90
    DBN-MF[34]83.7785.2684.5191.6990.43
    VGAMF[20]85.2385.5085.3692.8092.25
    本文工作VGAE-N89.5889.0989.3295.3895.21
    下载: 导出CSV
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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

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