基于功能网络信息传播预测疾病-miRNAs的关联
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摘要
为了快速发现与疾病关联的miRNA,基于功能网络信息传播,提出PMBP算法用于改进随机游走法,使用留一交叉验证评估了算法性能,最后进行案例分析.实验结果表明:对于尚未发现关联miRNA的疾病,随机游走法是失效的,而PMBP以疾病相似性作为先验信息,能够有效预测;对于已经关联miRNA的疾病,PMBP提高了预测性能,AUC值为0.866.对乳腺癌进行案例分析,预测的前50个miRNAs都被证实与乳腺癌相关,体现了PMBP算法的有效性.
In order to quickly find out disease-related miRNAs,PMBP algorithm was proposed for improving random walk based on functional network information propagation. Leave-one-out cross validation was utilized to evaluate the performance of the algorithm and finally a case was analyzed. The results showed that random walk is ineffective for diseases that have not yet been associated with miRNAs,but the miRNA can be effectively predicted by using disease similarities as prior information. For the diseases known to be related with miRNAs,PMPB achieves a better performance and the corresponding AUC value is 0. 866. In the case study of breast cancer,the predicted top 50 miRNAs are confirmed to be associated with breast cancer,which indicates the validity of PMBP.
In order to quickly find out disease-related miRNAs,PMBP algorithm was proposed for improving random walk based on functional network information propagation. Leave-one-out cross validation was utilized to evaluate the performance of the algorithm and finally a case was analyzed. The results showed that random walk is ineffective for diseases that have not yet been associated with miRNAs,but the miRNA can be effectively predicted by using disease similarities as prior information. For the diseases known to be related with miRNAs,PMPB achieves a better performance and the corresponding AUC value is 0. 866. In the case study of breast cancer,the predicted top 50 miRNAs are confirmed to be associated with breast cancer,which indicates the validity of PMBP.
引文
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[4]Jiang Q,Wang G,Jin S,et al.Predicting human microRNAdisease associations based on support vector machine[J].International Journal of Data Mining and Bioinformatics,2013,8(3):282-293.
[5]Zou Q,Li J J,Hong Q Q,et al.Prediction of MicroRNA-disease associations based on social network analysis methods[J].Bio Med Research International,2015,2015:810514.doi:10.1155/2015/810514.
[6]Chen X,Yan G Y.Semi-supervised learning for potential human microRNA-disease associations inference[J].Scientific Report,2014,4:5501.doi:10.1038/srep05501.
[7]Chen X,Yan C G,Zhang X T,et al.RBMMMDA:predicting multiple types of disease-microRNA associations[J].Scientific Report,2015,5:13877.doi:10.1038/srep13877.
[8]Chen H L,Zhang Z P.Similarity-based methods for potential human microRNA-disease association prediction[J].BMC Medical Genomics,2013,6(12):1-9.
[9]Sun D D,Li A,Feng H Q,et al.NTSMDA:prediction of miRNA-disease associations by integrating network topological similarity[J].Molecular Bio Systems,2016,12(7):2224-2232.
[10]Navlakha S,Kingsford C.The power of protein interaction networks for associating genes with diseases[J].Bioinformatics,2010,26(8):1057-1063.
[11]孟宪伟.MicroRNA与人类疾病关联的预测方法研究与实现[D].哈尔滨:哈尔滨工业大学,2012.(Meng Xian-wei.Research and implementation of predicting human disease-related microRNAs[D].Harbin:Harbin Institute of Technology,2012.)
[12]Chen X,Liu M X,Yan G Y.RWRMDA:predicting novel human microRNA-disease associations[J].Molecular Bio Systmes,2012,8(10):2792-2798.
[13]Xuan P,Han K,Guo Y D,et al.Prediction of potential diseaseassociated microRNAs based on random walk[J].Bioinformatics,2015,31(11):1805-1815.
[14]van Driel M A,Bruggeman J,Vriend G,et al.A text-mining analysis of the human phenome[J].European Journal of Human Genetics,2006,14(5):535-542.
[15]K9hler S,Schulz H,Krawitz P,et al.Clinical diagnostics in human genetics with semantic similarity searches in ontologies[J].The American Journal of Human Genetics,2009,85(4):457-464.
[16]Li J H,Lin X Y,Teng Y Y,et al.A comprehensive evaluation of disease phenotype networks for gene prioritization[J].PLOS One,2016,11(7):e0159457.doi:10.1371/journal.pone.0159457.
[17]Wang D,Wang J,Lu M,et al.Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases[J].Bioinformatics,2010,26(13):1644-1650.
[18]Vanunu O,Magger O,Ruppin E,et al.Associating genes and protein complexes with disease via network propagation[J].PLOS Computational Biology,2010,6(1):e1000641.