Predicting novel CircRNA-disease associations based on random walk and logistic regression model
Predicting novel CircRNA-disease associations based on random walk and logistic regression model
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DOI：
10.1016/j.compbiolchem.2020.107287
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发表时间：
2020-08-01
影响因子：
3.1
通讯作者：
Wu, Fang-Xiang
中科院分区：
文献类型：
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作者：
Ding, Yulian;Chen, Bolin;Wu, Fang-Xiang
环状RNA（circular RNA，circRNA）是一类内源性非编码RNA小分子，在人类基因组中具有调节蛋白质编码基因的功能。近年来，许多实验研究表明，circRNA在多种疾病中表达异常，可以作为疾病诊断和预后的生物标志物。然而，通过生物学实验来识别circRNA-疾病关联是昂贵且耗时的，并且很少有计算模型被提出用于新的circRNA-疾病关联预测。在这项研究中，我们开发了一个基于随机游走和逻辑回归（RWLR）的计算模型来预测circRNA与疾病的关联。首先，基于circRNA相关基因本体，计算circRNA与circRNA的功能相似度，构建circRNA-circRNA相似网络。然后，在circRNA相似网络上进行带重启的随机游走，并根据随机游走结果和circRNA-疾病关联矩阵提取每对circRNA-疾病的特征.最后，使用逻辑回归模型来预测新的circRNA-疾病关联。采用留一验证法（LOOCV）、5倍交叉验证法（5CV）和10倍交叉验证法（10 CV）对RWLR的预测性能进行了评价，并与最新的两种方法PWCDA和DWNN-RLS进行了比较。实验结果表明，本文提出的RWLR算法在LOOCV、5CV和10 CV下的AUC值均高于其他两种最新算法，表明RWLR算法具有更好的性能。此外，案例研究还说明了RWLR用于circRNA与疾病关联预测的可靠性和有效性。
Circular RNAs (circRNAs), a large group of small endogenous noncoding RNA molecules, have been proved to modulate protein-coding genes in the human genome. In recent years, many experimental studies have demonstrated that circRNAs are dysregulated in a number of diseases, and they can serve as biomarkers for disease diagnosis and prognosis. However, it is expensive and time-consuming to identify circRNA-disease associations by biological experiments and few computational models have been proposed for novel circRNA-disease association prediction. In this study, we develop a computational model based on the random walk and the logistic regression (RWLR) to predict circRNA-disease associations. Firstly, a circRNA-circRNA similarly network is constructed by calculating their functional similarity of circRNA based on circRNA-related gene ontology. Then, a random walk with restart is implemented on the circRNA similarly network, and the features of each pair of circRNA-disease are extracted based on the results of the random walk and the circRNA-disease association matrix. Finally, a logistic regression model is used to predict novel circRNA-disease associations. Leave one out validation (LOOCV), five-fold cross validation (5CV) and ten-fold cross validation (10CV) are adopted to evaluate the prediction performance of RWLR, by comparing with the latest two methods PWCDA and DWNN-RLS. The experiment results show that our RWLR has higher AUC values of LOOCV, 5CV and 10CV than the other two latest methods, which demonstrates that RWLR has a better performance than other computational methods. What's more, case studies also illustrate the reliability and effectiveness of RWLR for circRNA-disease association prediction.