计算机科学 ›› 2021, Vol. 48 ›› Issue (10): 114-120.doi: 10.11896/jsjkx.200900169
刘丹, 赵森, 颜志良, 赵静, 王会青
LIU Dan, ZHAO Sen, YAN Zhi-liang, ZHAO Jing, WANG Hui-qing
摘要: 作为一类小的非编码RNA,miRNA的异常调控与人类疾病的发生和发展密切相关,研究miRNA与疾病的关联对于了解人类疾病致病机制具有重要意义。机器学习方法被广泛应用于miRNA-疾病关联预测,然而现有方法仅仅考虑了miRNA与疾病相似性网络信息,忽略了相似性网络的拓扑结构。因此,文中提出基于堆叠自动编码器的miRNA-疾病关联预测模型SAEMDA,该模型采用重启随机游走获取miRNA与疾病相似性网络的拓扑结构特征,用堆叠自动编码器提取miRNA与疾病的抽象低维特征,将得到的低维特征输入深度神经网络进行miRNA-疾病关联预测。SAEMDA模型在5折交叉验证中取得了较好的结果,并在结肠癌和肺癌两个案例中进行了验证。在结肠癌的案例中,此模型预测的前50个miRNA-疾病关联中的45个miRNA在数据库中得到了验证;在肺癌的案例中,排名前50的miRNA均在数据库中得到了验证。
中图分类号:
[1]ZHANG J X,SONG W,CHEN Z H,et al.Prognostic and predictive value of a microRNA signature in stage II colon cancer:a microRNA expression analysis[J].The Lancet Oncology,2013,14(13):1295-1306. [2]SU Y,DENG M F,XIONG W,et al.MicroRNA-26a/death-associated protein kinase 1 signaling induces synucleinopathy and dopaminergic neuron degeneration in Parkinson's disease[J].Biological Psychiatry,2019,85(9):769-781. [3]YOU Z,HUANG Z A,ZHU Z X,et al.PBMDA:A novel and effective path-based computational model for miRNA-disease association prediction[J].PLoS Computational Biology,2017,13(3):e1005455. [4]CHEN X,LIU M X,YAN G Y.RWRMDA:predicting novel humanmicroRNA-disease associations[J].Molecular Biosystems,2012,8(10):2792-2798. [5]CHEN X,WANG C C,YIN J,et al.Novel human miRNA-disease association inference based on random forest[J].Mole-cular Therapy-Nucleic Acids,2018,13:568-579. [6]YAO D,ZHAN X,KWOH C K.An improved random forest-based computational model for predicting novel miRNA-disease associations[J].BMC Bioinformatics,2019,20(1):1-14. [7]ZHANG L,CHEN X,YIN J.Prediction of Potential miRNA-Disease Associations Through a Novel Unsupervised Deep Learning Framework with Variational Autoencoder[J].Cells,2019,8(9):1040. [8]PENG J,HUI W,LI Q,et al.A learning-based framework for miRNA-disease association identification using neural networks[J].Bioinformatics,2019,35(21):4364-4371. [9]CHEN X,GONG Y,ZHANG D H,et al.DRMDA:deep representations-based miRNA-disease association prediction[J].Journal of Cellular and Molecular Medicine,2017,22(1):472-485. [10]WANG L,XU T,SONG C D.Prediction algorithm of miRNA and disease correlation based on deep learning[J].Acta Electronica Sinica,2020,447(5):40-47. [11]KÖHLER S,BAUER S,HORN D,et al.Walking the interactome for prioritization of candidate disease genes[J].The Ameri-can Journal of Human Genetics,2008,82(4):949-958. [12]TANG J Q,WU J L,LIAO Y X,et al.Protein function prediction based on double weighted voting[J].Computer Science,2019,46(4):222-227. [13]WANG H,LE Z C,GONG X,et al.Summary of link prediction methods based on feature classification[J].Computer Science,2020,47(8):302-312. [14]JIANG L,DING Y,TANG J,et al.MDA-SKF:similarity kernel fusion for accurately discovering miRNA-disease association[J].Frontiers in Genetics,2018,9:618. [15]LI Y,QIU C,TU J,et al.HMDD v2.0:a database for experimentally supported human microRNA and disease associations[J].Nucleic Acids Research,2014,42(D1):D1070-D1074. [16]YANG Z,REN F,LIU C,et al.dbDEMC:a database of differentially expressed miRNAs in human cancers[J].BMC Geno-mics,2010,11(4):1-8. [17]WANG D,WANG J,LU M,et al.Inferring the human micro-RNA functional similarity and functional network based on microRNA-associated diseases[J].Bioinformatics,2010,26(13):1644-1650. [18]RUAN L,XIONG Y.Research on Functional Similarity of mi-RNA Based on Network Representation Learning[J].Computer Engineering,2019,45(2):154-159. [19]ZHU Y X,FENG W,GUO X H.Application progress of deep learning method in brain image of Alzheimer's disease[J].Me-dical Review,2019,25(18):3562-3566. [20]LI Y N,HU Y J,GAN W,et al.Survey on Target Site Prediction of Human miRNA Based on Deep Learning[J].Computer Science,2021,48(1):209-216. [21]XUAN P,DONG Y,GUO Y,et al.Dual convolutional neural network based method for predicting disease-related miRNAs[J].International Journal of Molecular Sciences,2018,19(12):3732. [22]YU Y,NANGIA-MAKKER P,FARHANA L,et al.miR-21 and miR-145 cooperation in regulation of colon cancer stem cells[J].Molecular Cancer,2015,14(1):1-11. [23]BRAY F,FERLAY J,SOERJOMATARAM I,et al.Globalcancer statistics 2018:GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J].CA:a Cancer Journal for Clinicians,2018,68(6):394-424. |
[1] | 袁榕, 宋玉蓉, 孟繁荣. 一种基于加权网络拓扑权重的链路预测方法 Link Prediction Method Based on Weighted Network Topology Weight 计算机科学, 2020, 47(5): 265-270. https://doi.org/10.11896/jsjkx.190600031 |
[2] | 刘晓东, 魏海平, 曹宇. 考虑网络拓扑结构变化的SIRS模型的建立与稳定性分析 Modeling and Stability Analysis for SIRS Model with Network Topology Changes 计算机科学, 2019, 46(6A): 375-379. |
[3] | 封云飞, 陈红梅. 基于拓扑结构的密度峰值重叠社区发现算法 Topological Structure Based Density Peak Algorithm for Overlapping Community Detection 计算机科学, 2019, 46(10): 39-48. https://doi.org/10.11896/jsjkx.180901644 |
[4] | 郭利娟, 吕晓琳. 线性拓扑结构的乐观认证邮件 Optimistic Certified Email for Line Topology 计算机科学, 2018, 45(8): 156-159. https://doi.org/10.11896/j.issn.1002-137X.2018.08.028 |
[5] | 洪汉玉,马尔威,黄丽坤. 基于重新检测过程的三维细化算法的改进 Improvement of 3D Thinning Algorithm Based on Re-checking Procedure 计算机科学, 2018, 45(5): 266-272. https://doi.org/10.11896/j.issn.1002-137X.2018.05.046 |
[6] | 万莹, 洪玫, 陈宇星, 王帅, 樊哲宁. 基于时间、空间和规则的无线网络告警关联方法 Wireless Network Alarm Correlation Based on Time,Space and Rules 计算机科学, 2018, 45(11A): 287-291. |
[7] | 张光兰, 杨秋辉, 程雪梅, 姜科, 王帅, 谭武坤. 序列模式挖掘在通信网络告警预测中的应用 Application of Sequence Pattern Mining in Communication Network Alarm Prediction 计算机科学, 2018, 45(11A): 535-538. |
[8] | 焦重阳,周清雷,张文宁. 混合拓扑结构的粒子群算法及其在测试数据生成中的应用研究 MPSO and Its Application in Test Data Automatic Generation 计算机科学, 2017, 44(12): 249-254. https://doi.org/10.11896/j.issn.1002-137X.2017.12.045 |
[9] | 徐静,刘宴涛,夏桂阳,Y asser MORGAN. 基于网络编码的拓扑推断研究综述 Network Coding Based Topology Inference:A Survey 计算机科学, 2016, 43(Z6): 242-248. https://doi.org/10.11896/j.issn.1002-137X.2016.6A.059 |
[10] | 汤颖,钟南江,范菁. 一种结合用户评分信息的改进好友推荐算法 Improved Friends Recommendation Algorithm Combining with User Rating Information 计算机科学, 2016, 43(9): 111-115. https://doi.org/10.11896/j.issn.1002-137X.2016.09.021 |
[11] | 徐喜荣,黄亚真,张思佳,董学智. 广义Kautz有向图GK(3,n)的反馈数的界 Feedback Numbers of Generalized Kautz Digraphs GK(3,n) 计算机科学, 2016, 43(5): 13-21. https://doi.org/10.11896/j.issn.1002-137X.2016.05.003 |
[12] | 徐潜,谭成翔. 基于动态权限集的Android强制访问控制模型 Mandatory Access Control Model for Android Based on Dynamic Privilege Set 计算机科学, 2015, 42(11): 191-196. https://doi.org/10.11896/j.issn.1002-137X.2015.11.040 |
[13] | 范青刚,叶雪梅,蔡艳宁. Zigbee路由协议在车载自组网监控系统中的性能研究 Performance Evaluation of Zigbee Routing Protocol in VANET Monitoring System 计算机科学, 2014, 41(Z6): 326-328. |
[14] | 李洪兵,熊庆宇,石为人. 无线传感器网络非均匀等级分簇拓扑结构研究 Study on Topology with Non-uniform Hierarchical Clustering for Wireless Sensor Networks 计算机科学, 2013, 40(2): 49-52. |
[15] | 曹洪新,李光顺,吴俊华. 基于一种新网络拓扑结构的低功耗研究 Low Power Research Based on a New NoC Topology Architecture 计算机科学, 2012, 39(Z11): 327-330. |
|