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Predicting human disease-associated circRNAs based on locality-constrained linear coding

机译：基于地方约束线性编码预测人类疾病相关的Circrnas

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摘要

Circular RNAs (circRNAs) are a new kind of endogenous non-coding RNAs, which have been discovered continuously. More and more studies have shown that circRNAs are related to the occurrence and development of human diseases. Identification of circRNAs associated with diseases can contribute to understand the pathogenesis, diagnosis and treatment of diseases. However, experimental methods of circRNA prediction remain expensive and time-consuming. Therefore, it is urgent to propose novel computational methods for the prediction of circRNA-disease associations. In this study, we develop a computational method called LLCDC that integrates the known circRNA-disease associations, circRNA semantic similarity network, disease semantic similarity network, reconstructed circRNA similarity network, and reconstructed disease similarity network to predict circRNAs related to human diseases. Specifically, the reconstructed similarity networks are obtained by using Locality-Constrained Linear Coding (LLC) on the known association matrix, cosine similarities of circRNAs and diseases. Then, the label propagation method is applied to the similarity networks, and four relevant score matrices are respectively obtained. Finally, we use 5-fold cross validation (5-fold CV) to evaluate the performance of LLCDC, and the AUC value of the method is 0.9177, indicating that our method performs better than the other three methods. In addition, case studies on gastric cancer, breast cancer and papillary thyroid carcinoma further verify the reliability of our method in predicting disease-associated circRNAs.
机译：圆形RNA（CircRNA）是一种新的内源性非编码RNA，其已被连续被发现。越来越多的研究表明，Circrnas与人类疾病的发生和发展有关。鉴定与疾病相关的CircrNA可以有助于了解疾病的发病机制，诊断和治疗。然而，Circrna预测的实验方法仍然昂贵且耗时。因此，迫切需要提出用于预测Circrna疾病关联的新型计算方法。在本研究中，我们开发一种称为LLCDC的计算方法，其集成了已知的循环疾病关联，CircRNA语义相似性网络，疾病语义相似性网络，重建的CircrNA相似性网络，以及重建的疾病相似性网络，以预测与人类疾病相关的CircrNA。具体地，通过在已知的关联矩阵，余弦相似度的CircrNA和疾病上使用地区限制的线性编码（LLC）来获得重建的相似性网络。然后，将标签传播方法应用于相似度网络，并且分别获得四个相关的分数矩阵。最后，我们使用5倍交叉验证（5倍CV）来评估LLCDC的性能，而该方法的AUC值为0.9177，表明我们的方法比其他三种方法更好。此外，胃癌，乳腺癌和乳头状甲状腺癌的案例研究进一步验证了我们预测疾病相关的Circrnas方法的可靠性。

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