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  • CN 31-1691/TQ

混合线性非线性网络的多源miRNA-疾病关联预测方法

赵静 李浩琳 王会青 王彬

赵静, 李浩琳, 王会青, 王彬. 混合线性非线性网络的多源miRNA-疾病关联预测方法[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cnki.1006-3080.20220115004
引用本文: 赵静, 李浩琳, 王会青, 王彬. 混合线性非线性网络的多源miRNA-疾病关联预测方法[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cnki.1006-3080.20220115004
ZHAO Jing, LI Haolin, WANG Huiqing, WANG Bin. A Hybrid Linear and Nonlinear Network-based Method on Multi-source MiRNA-Disease Association Prediction[J]. Journal of East China University of Science and Technology. doi: 10.14135/j.cnki.1006-3080.20220115004
Citation: ZHAO Jing, LI Haolin, WANG Huiqing, WANG Bin. A Hybrid Linear and Nonlinear Network-based Method on Multi-source MiRNA-Disease Association Prediction[J]. Journal of East China University of Science and Technology. doi: 10.14135/j.cnki.1006-3080.20220115004

混合线性非线性网络的多源miRNA-疾病关联预测方法

doi: 10.14135/j.cnki.1006-3080.20220115004
基金项目: 山西省自然科学基金青年基金项目(20210302124272) 自然科学研究面上项目(20210302123099)
详细信息
    作者简介:

    赵静(1996—),女,山东潍坊人,硕士生,主要研究方向为深度学习与生物信息处理。E-mail:1565538063@qq.com

    通讯作者:

    王会青, 1013208257@qq.com

  • 中图分类号: TP391

A Hybrid Linear and Nonlinear Network-based Method on Multi-source MiRNA-Disease Association Prediction

  • 摘要: 现有miRNA-疾病关联研究大多采用miRNA功能和疾病语义相似性作为输入,未考虑miRNA序列和疾病功能等相似性信息;在特征提取过程中忽略了线性与非线性特征间的信息互补,影响特征质量。因此提出一种miRNA-疾病关联预测模型GCNMSF,基于miRNA和疾病多源相似性信息,融合嵌入卷积注意块的图卷积网络学习的非线性特征和非负矩阵分解方法学习的线性特征,实现信息互补,以预测miRNA-疾病关联。实验结果表明,GCNMSF模型优于现有的miRNA-疾病关联预测方法,可以有效预测miRNA-疾病关联。

     

  • 图  1  GCNMSF模型架构

    Figure  1.  The model framework of GCNMSF

    图  2  CBAM通道注意模块

    Figure  2.  The channel attention module in CBAM

    图  3  CBAM空间注意模块

    Figure  3.  The spatial attention module in CBAM

    图  4  GCNMSF模型的参数选择实验结果

    Figure  4.  Experimental results of the GCNMSF model with different parameters

    图  5  线性和非线性特征学习模块消融实验结果

    Figure  5.  Results of linear and nonlinear feature learning module ablation experiments

    图  6  不同方法五折交叉实验结果

    Figure  6.  Five-fold-cross experiments of different methods

    图  7  不同方法预测特定疾病的实验结果

    Figure  7.  Experimental results of different methods on specific diseases prediction

    表  1  miRNA和疾病多源相似性数据

    Table  1.   Multi-source similarities of miRNA and disease

    SimilarityDatabaseDimension
    miRNA functional similarity $ {K}_{m,1} $MISIM495×495
    miRNA sequence similarity $ {K}_{m,2} $miRBase495×495
    Disease semantic similarity $ {K}_{d,1} $MeSH383×383
    Disease functional similarity $ {K}_{d,2} $HumanNet383×383
    miRNA hamming similarity $ {K}_{m,3} $495×495
    Disease hamming similarity $ {K}_{d,3} $383×383
    下载: 导出CSV

    表  2  不同相似性组合消融实验数据表

    Table  2.   Ablation experiments with different similarity combinations

    Similarity combinationAUCAUPRF1_score
    MSS+DSS0.93140.93240.8572
    MFS+DSS0.93220.93240.8594
    MSS+DFS0.93810.93830.8693
    MFS+DFS0.93640.93740.8659
    MSS+DFS+DFS+DSS0.93940.93910.8708
    MSS+DFS+DFS+DSS+HMS0.94520.94700.8748
    下载: 导出CSV

    表  3  肺癌相关miRNA预测实验数据表

    Table  3.   The top 50 predicted miRNAs associated with lung cancer

    RankmiRNADatabaseRankmiRNADatabase
    1hsa-mir-16dbDEMC3; miR2Disease26hsa-mir-328dbDEMC3
    2hsa-mir-195dbDEMC3; miR2Disease27hsa-mir-148bdbDEMC3
    3hsa-mir-106bdbDEMC328hsa-mir-23bdbDEMC3
    4hsa-mir-193bdbDEMC329hsa-mir-99adbDEMC3; miR2Disease
    5hsa-mir-141dbDEMC3; miR2Disease30hsa-mir-196bdbDEMC3
    6hsa-mir-15adbDEMC331hsa-mir-302adbDEMC3
    7hsa-mir-302bdbDEMC332hsa-mir-452dbDEMC3
    8hsa-mir-451adbDEMC333hsa-mir-122dbDEMC3
    9hsa-mir-429dbDEMC3; miR2Disease34hsa-mir-520adbDEMC3
    10hsa-mir-378aunconfirm35hsa-mir-152dbDEMC3
    11hsa-mir-342dbDEMC336hsa-mir-194dbDEMC3
    12hsa-mir-296unconfirm37hsa-mir-215dbDEMC3;
    13hsa-mir-320adbDEMC338hsa-mir-92bdbDEMC3
    14hsa-mir-151aunconfirm39hsa-mir-376cdbDEMC3
    15hsa-mir-204dbDEMC3; miR2Disease40hsa-mir-520ddbDEMC3
    16hsa-mir-302cdbDEMC341hsa-mir-367dbDEMC3
    17hsa-mir-149dbDEMC342hsa-mir-708dbDEMC3
    18hsa-mir-130adbDEMC3; miR2Disease43hsa-mir-345dbDEMC3; miR2Disease
    19hsa-mir-625dbDEMC344hsa-mir-423dbDEMC3; miR2Disease
    20hsa-mir-15bdbDEMC345hsa-mir-520cunconfirm
    21hsa-mir-20bdbDEMC346hsa-mir-650dbDEMC3; miR2Disease
    22hsa-mir-10adbDEMC347hsa-mir-130bdbDEMC3
    23hsa-mir-129dbDEMC348hsa-mir-302ddbDEMC3
    24hsa-mir-373dbDEMC349hsa-mir-449bdbDEMC3
    25hsa-mir-139dbDEMC3; miR2Disease50hsa-mir-520bdbDEMC3; miR2Disease
    下载: 导出CSV

    表  4  乳腺癌相关miRNA预测实验数据表

    Table  4.   The top 50 predicted miRNAs associated with breast cancer

    RankmiRNADatabaseRankmiRNADatabase
    1hsa-mir-21dbDEMC3; miR2Disease26hsa-mir-182dbDEMC3; miR2Disease
    2hsa-mir-155dbDEMC3; miR2Disease27hsa-let-7cdbDEMC3
    3hsa-mir-17dbDEMC3; miR2Disease28hsa-mir-223dbDEMC3
    4hsa-mir-145dbDEMC3; miR2Disease29hsa-mir-210dbDEMC3; miR2Disease
    5hsa-mir-34adbDEMC330hsa-mir-19bdbDEMC3
    6hsa-mir-125bdbDEMC3; miR2Disease31hsa-mir-27adbDEMC3; miR2Disease
    7hsa-mir-20adbDEMC3; miR2Disease32hsa-mir-124dbDEMC3; miR2Disease
    8hsa-mir-146adbDEMC3; miR2Disease33hsa-mir-200adbDEMC3; miR2Disease
    9hsa-mir-126dbDEMC3; miR2Disease34hsa-mir-199adbDEMC3
    10hsa-let-7adbDEMC3; miR2Disease35hsa-mir-146bmiR2Disease
    11hsa-mir-221dbDEMC3; miR2Disease36hsa-let-7edbDEMC3
    12hsa-mir-92adbDEMC337hsa-let-7gdbDEMC3
    13hsa-mir-200cdbDEMC3; miR2Disease38hsa-mir-10bdbDEMC3; miR2Disease
    14hsa-mir-16dbDEMC339hsa-mir-30adbDEMC3
    15hsa-mir-18adbDEMC3; miR2Disease40hsa-mir-101dbDEMC3
    16hsa-mir-143dbDEMC3; miR2Disease41hsa-mir-29bdbDEMC3; miR2Disease
    17hsa-mir-31dbDEMC3; miR2Disease42hsa-mir-181bdbDEMC3; miR2Disease
    18hsa-mir-200bdbDEMC3; miR2Disease43hsa-mir-196adbDEMC3; miR2Disease
    19hsa-mir-1dbDEMC344hsa-mir-148adbDEMC3; miR2Disease
    20hsa-mir-34cdbDEMC345hsa-mir-379dbDEMC3
    21hsa-mir-222dbDEMC3; miR2Disease46hsa-mir-183dbDEMC3
    22hsa-mir-19adbDEMC347hsa-mir-15adbDEMC3
    23hsa-mir-29adbDEMC348hsa-mir-1469dbDEMC3
    24hsa-mir-218dbDEMC349hsa-mir-34bdbDEMC3
    25hsa-let-7bdbDEMC350hsa-mir-195dbDEMC3; miR2Disease
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-01-15
  • 网络出版日期:  2022-04-24

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