Zhu Xianyou, Wang Xuzai, Zhao Haochen, Pei Tingrui, Kuang Linai, Wang Lei
College of Computer Science and Technology, Hengyang Normal University, Hengyang, China.
Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, China.
Front Genet. 2020 Apr 28;11:384. doi: 10.3389/fgene.2020.00384. eCollection 2020.
Recent studies have indicated that microRNAs (miRNAs) are closely related to sundry human sophisticated diseases. According to the surmise that functionally similar miRNAs are more likely associated with phenotypically similar diseases, researchers have proposed a variety of valid computational models through integrating known miRNA-disease associations, disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity to discover the potential miRNA-disease relationships in biomedical researches. Taking account of the limitations of previous computational models, a new computational model based on biased heat conduction for MiRNA-Disease Association prediction (BHCMDA) was proposed in this paper, which can achieve the AUC of 0.8890 in LOOCV (Leave-One-Out Cross Validation) and the mean AUC of 0.9060, 0.8931 under the framework of twofold cross validation, fivefold cross validation, respectively. In addition, BHCMDA was further implemented to the case studies of three vital human cancers, and simulation results illustrated that there were 88% (Esophageal Neoplasms), 92% (Colonic Neoplasms) and 92% (Lymphoma) out of top 50 predicted miRNAs having been confirmed by experimental literatures, separately, which demonstrated the good performance of BHCMDA as well. Thence, BHCMDA would be a useful calculative resource for potential miRNA-disease association prediction.
最近的研究表明，微小RNA（miRNA）与各种人类复杂疾病密切相关。基于功能相似的miRNA更有可能与表型相似的疾病相关的推测，研究人员通过整合已知的miRNA-疾病关联、疾病语义相似性、miRNA功能相似性和高斯相互作用谱核相似性，提出了多种有效的计算模型，以发现生物医学研究中潜在的miRNA-疾病关系。考虑到先前计算模型的局限性，本文提出了一种基于偏热传导的miRNA-疾病关联预测计算模型（BHCMDA），该模型在留一法交叉验证（LOOCV）中的AUC为0.8890，在二倍交叉验证、五倍交叉验证框架下的平均AUC分别为0.9060、0.8931。此外，BHCMDA进一步应用于三种重要人类癌症的案例研究，模拟结果表明，在前50个预测的miRNA中，分别有88%（食管肿瘤）、92%（结肠肿瘤）和92%（淋巴瘤）已被实验文献证实，这也证明了BHCMDA的良好性能。因此，BHCMDA将成为潜在miRNA-疾病关联预测的有用计算资源。
