【摘要】：microRNAs(miRNAs)在生命進(jìn)程中發(fā)揮著重要作用.近年來,預(yù)測miRNAs與疾病的關(guān)聯(lián)關(guān)系成為一個(gè)研究熱點(diǎn).當(dāng)前,計(jì)算方法整體上可以分為兩大類:基于相似度度量的方法和基于機(jī)器學(xué)習(xí)的方法.前者通過度量網(wǎng)絡(luò)中節(jié)點(diǎn)之間的關(guān)聯(lián)強(qiáng)度預(yù)測miRNA-疾病關(guān)聯(lián),但需要構(gòu)建高質(zhì)量的生物網(wǎng)絡(luò)模型;后者將機(jī)器學(xué)習(xí)相關(guān)算法應(yīng)用到這個(gè)問題中,但需要構(gòu)建高可信度的負(fù)例集合.基于以上困難和不足,提出了一種計(jì)算模型BNPDCMDA,用于預(yù)測miRNAs-疾病關(guān)聯(lián)關(guān)系.該方法首先構(gòu)建miRNA-疾病雙層網(wǎng)絡(luò)模型,然后利用miRNA的功能相似度對其進(jìn)行基于密度的聚類,進(jìn)而將二分網(wǎng)絡(luò)投影應(yīng)用于聚類后的miRNAs及疾病集合構(gòu)成的miRNA-疾病雙層子網(wǎng)中,最終完成對miRNA與疾病關(guān)聯(lián)關(guān)系的預(yù)測.實(shí)驗(yàn)結(jié)果表明,采用留一交叉驗(yàn)證法得到的AUC值可達(dá)99.08%,明顯優(yōu)于當(dāng)前其他高效方法.最后,采用BNPDCMDA方法對某些常見疾病所關(guān)聯(lián)的miRNAs進(jìn)行預(yù)測,實(shí)驗(yàn)結(jié)果獲得了文獻(xiàn)的支持,進(jìn)一步表明了該方法的有效性.
[Abstract]:MicroRNAs (miRNAs) plays an important role in the process of life. In recent years, predicting the relationship between miRNAs and disease has become a hot research topic. At present, the calculation methods can be divided into two categories as a whole: the method based on similarity measure and the method based on machine learning. The former forecasts miRNA- disease association by measuring the correlation strength between nodes in the network, but it is necessary to construct a high quality biological network model, while the latter applies the machine learning correlation algorithm to this problem, but needs to construct a negative case set with high reliability. Based on the above difficulties and shortcomings, a computational model BNPDCMDA, is proposed to predict the association of miRNAs- diseases. In this method, the double-layer network model of miRNA- disease is constructed, and then the density-based clustering is carried out by using the functional similarity of miRNA, and then the binary network projection is applied to the double-layer subnet of miRNA- disease formed by clustering miRNAs and disease set, and finally the prediction of the relationship between miRNA and disease is completed. The experimental results show that the AUC value obtained by the method of residual cross verification can reach 99.08%, which is obviously better than that of other efficient methods at present. Finally, the BNPDCMDA method is used to predict the miRNAs associated with some common diseases, and the experimental results are supported by the literature, which further shows the effectiveness of the method.
【作者單位】： 北京建筑大學(xué)電氣與信息工程學(xué)院;哈爾濱工業(yè)大學(xué)計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院;
【基金】：國家自然科學(xué)基金(61571163,61532014,61671189,61402132) 國家重點(diǎn)基礎(chǔ)研究發(fā)展計(jì)劃(973)(2016YFC09019 02)~~
【分類號】：R3416;TP311.13

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