北京生物醫(yī)學(xué)工程

基于機(jī)器學(xué)習(xí)方法的非編碼RNA-蛋白質(zhì)相互作用的預(yù)測

Prediction of ncRNA-protein interactions based on machine learning methods

作者：程淑萍譚建軍門婧睿

單位：北京工業(yè)大學(xué)生命科學(xué)與生物工程學(xué)院，智能化生理測量與臨床轉(zhuǎn)化北京市國際科研合作基地（北京 100124）

關(guān)鍵詞：非編碼RNA-蛋白質(zhì)相互作用； LightGBM；隨機(jī)森林；極端梯度增強(qiáng)算法；卷積自編碼器

分類號(hào)：R318.01; Q51

出版年·卷·期（頁碼）：2019·38·4（353-359）

摘要：

目的非編碼RNA-蛋白質(zhì)的相互作用（noncoding RNA-protein interactions, ncRPI）具有重要的生物學(xué)意義，目前預(yù)測其相互作用已成為當(dāng)下研究非編碼RNA (noncoding RNA, ncRNA)和蛋白質(zhì)功能的重要途徑之一。方法本研究基于ncRNA和蛋白質(zhì)的序列信息提取特征，運(yùn)用卷積自編碼器預(yù)處理原始數(shù)據(jù)，訓(xùn)練三個(gè)機(jī)器學(xué)習(xí)模型: LightGBM(LBM)、隨機(jī)森林(random forest, RF)和極端梯度增強(qiáng)算法(extreme gradient boosting, XGB), 預(yù)測ncRNA與蛋白質(zhì)的相互作用。結(jié)果在RPI369和RPI488兩個(gè)數(shù)據(jù)集做5倍交叉驗(yàn)證，LBM、RF與XGB三個(gè)模型在兩個(gè)數(shù)據(jù)集均達(dá)到較高的預(yù)測準(zhǔn)確率，在RPI369數(shù)據(jù)集三個(gè)模型的預(yù)測準(zhǔn)確率分別為0.757（LBM）、0.791(RF)、0.791(XGB)，在RPI488數(shù)據(jù)集三個(gè)模型的預(yù)測準(zhǔn)確率分別為0.918(LBM)、0.908(RF)、0.918(XGB)；三個(gè)模型在RPI1807、RPI2241、RPI13254大數(shù)據(jù)集也取得較高的AUC(area under curve)值，在RPI1807三個(gè)模型的AUC值均為0.99，在RPI2241三個(gè)模型最低AUC值為0.87，在RPI13254三個(gè)模型最低AUC值為0.81，都表現(xiàn)出較好的預(yù)測準(zhǔn)確性。結(jié)論機(jī)器學(xué)習(xí)方法能夠預(yù)測ncRNA與蛋白質(zhì)是否存在相互作用。

Objective The biological significance of noncoding RNA-protein interactions (ncRPI) is important, and ncRPI prediction is an important way to study the function of noncoding RNA (ncRNA) and protein. Methods We extracted feature based on the sequence of ncRNA and protein in the work, preprocessed raw data by training a convolutional autoencoder (CAE). Three machine learning models, LightGBM (LBM), random forest (RF) and extreme gradient boosting (XGB) were trained to predict the ncRPI. Results We tested the three models by 5-fold cross validation (CV) on RPI369 and RPI488. All the three methods of LBM, RF and XGB achieved high performance with the accuracy of 0.757 (LBM), 0.791 (RF), 0.791 (XGB) on RPI369, respectively. On RPI488, the three models obtained the accuracy of 0.918 (LBM), 0.908 (RF), 0.918 (XGB), respectively. The three models obtained higher area under curve (AUC) on large-scale data. On RPI1807, all the three models obtained the AUC of 0.99, and the smallest AUC of 0.87 and 0.81 on RPI2241 and RPI13254, respectively. All the three methods of LBM, RF and XGB performed well for predicting ncRPI. Conclusions The machine learning methods can be used to predict ncRNA-protein interaction.

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