Chen Min, Peng Yan, Li Ang, Li Zejun, Deng Yingwei, Liu Wenhua, Liao Bo, Dai Chengqiu
College of Computer Science and Technology, Hunan Institute of Technology 421002 Hengyang China
College of Information Science and Engineering, Hunan University Changsha 410082 China
RSC Adv. 2018 Oct 30;8(64):36675-36690. doi: 10.1039/c8ra07519k. eCollection 2018 Oct 26.
The abnormal expression of miRNAs is directly related to the development of human diseases. Predicting the potential candidate miRNAs associated with diseases can contribute to the detection, diagnosis, treatment and prevention of human complex diseases. The effective inference of the calculation method of the relationship between miRNAs and diseases is an effective supplement to biological experiments. It is of great help in the prevention, treatment and prognosis of complex diseases. This paper proposes a novel information diffusion method based on network consistency (IDNC) for identifying disease related microRNAs. The model first synthesizes the miRNA family information and the miRNA function similarity to reconstruct the miRNA network, and reconstruct the disease network by using the known disease and miRNA-related information and the semantic score between diseases. Then the global similarity of the two networks is obtained by using the Laplacian score of graphs. The global similarity score is a measure of the similarity between diseases and miRNAs. The disease-miRNA relation network was reconstructed by integrating the global similarity relation. The network consistency diffusion seed is then obtained by combining the global similarity network with the reconstructed disease-miRNA association network. Thereafter, the stable diffusion spectrum is generated as the prediction score by using the restarted random walk algorithm. The AUC value obtained by performing the LOOCV in the gold benchmark dataset is 0.8814. The AUC value obtained by performing the LOOCV in the predictive dataset is 0.9512. Compared with other frontier methods, our method has higher accuracy, which is further illustrated by case studies of breast neoplasms and colon neoplasms to prove that IDNC is valuable.
微小RNA（miRNA）的异常表达与人类疾病的发生直接相关。预测与疾病相关的潜在候选miRNA有助于人类复杂疾病的检测、诊断、治疗和预防。有效推断miRNA与疾病之间关系的计算方法是对生物学实验的有效补充，对复杂疾病的预防、治疗和预后有很大帮助。本文提出了一种基于网络一致性的新型信息扩散方法（IDNC）来识别与疾病相关的微小RNA。该模型首先综合miRNA家族信息和miRNA功能相似性来重建miRNA网络，并利用已知的疾病与miRNA相关信息以及疾病之间的语义得分来重建疾病网络。然后通过使用图的拉普拉斯分数获得两个网络的全局相似性。全局相似性得分是衡量疾病与miRNA之间相似性的指标。通过整合全局相似性关系重建疾病-miRNA关系网络。然后将全局相似性网络与重建的疾病-miRNA关联网络相结合，得到网络一致性扩散种子。此后，使用重启随机游走算法生成稳定扩散谱作为预测分数。在黄金基准数据集中进行留一法交叉验证（LOOCV）得到的AUC值为0.8814。在预测数据集中进行留一法交叉验证得到的AUC值为0.9512。与其他前沿方法相比，我们的方法具有更高的准确性，通过乳腺肿瘤和结肠肿瘤的案例研究进一步证明了IDNC是有价值的。
