Zeng Xiangxiang, Wang Wen, Deng Gaoshan, Bing Jiaxin, Zou Quan
Shenzhen Research Institute of Xiamen University, Xiamen University, Shenzhen 518000, Guangdong, China; Department of Information Science and Technology, Xiamen University, Xiamen 361005, Fujian, China.
Shenzhen Research Institute of Xiamen University, Xiamen University, Shenzhen 518000, Guangdong, China.
Mol Ther Nucleic Acids. 2019 Jun 7;16:566-575. doi: 10.1016/j.omtn.2019.04.010. Epub 2019 Apr 18.
Identifying disease-related microRNAs (miRNAs) is an essential but challenging task in bioinformatics research. Much effort has been devoted to discovering the underlying associations between miRNAs and diseases. However, most studies mainly focus on designing advanced methods to improve prediction accuracy while neglecting to investigate the link predictability of the relationships between miRNAs and diseases. In this work, we construct a heterogeneous network by integrating neighborhood information in the neural network to predict potential associations between miRNAs and diseases, which also consider the imbalance of datasets. We also employ a new computational method called a neural network model for miRNA-disease association prediction (NNMDA). This model predicts miRNA-disease associations by integrating multiple biological data resources. Comparison of our work with other algorithms reveals the reliable performance of NNMDA. Its average AUC score was 0.937 over 15 diseases in a 5-fold cross-validation and AUC of 0.8439 based on leave-one-out cross-validation. The results indicate that NNMDA could be used in evaluating the accuracy of miRNA-disease associations. Moreover, NNMDA was applied to two common human diseases in two types of case studies. In the first type, 26 out of the top 30 predicted miRNAs of lung neoplasms were confirmed by the experiments. In the second type of case study for new diseases without any known miRNAs related to it, we selected breast neoplasms as the test example by hiding the association information between the miRNAs and this disease. The results verified 50 out of the top 50 predicted breast-neoplasm-related miRNAs.
识别与疾病相关的微小RNA（miRNA）是生物信息学研究中一项重要但具有挑战性的任务。人们已付出诸多努力来发现miRNA与疾病之间的潜在关联。然而，大多数研究主要集中在设计先进方法以提高预测准确性，却忽略了研究miRNA与疾病关系的链接可预测性。在这项工作中，我们通过整合神经网络中的邻域信息构建了一个异质网络，以预测miRNA与疾病之间的潜在关联，同时也考虑了数据集的不平衡问题。我们还采用了一种名为用于miRNA - 疾病关联预测的神经网络模型（NNMDA）的新计算方法。该模型通过整合多种生物数据资源来预测miRNA - 疾病关联。将我们的工作与其他算法进行比较，揭示了NNMDA的可靠性能。在5折交叉验证中，其在15种疾病上的平均AUC分数为0.937，基于留一法交叉验证的AUC为0.8439。结果表明，NNMDA可用于评估miRNA - 疾病关联的准确性。此外，NNMDA还应用于两种类型案例研究中的两种常见人类疾病。在第一种类型中，肺癌前30个预测的miRNA中有26个通过实验得到证实。在第二种针对没有任何已知相关miRNA的新疾病的案例研究中，我们通过隐藏miRNA与该疾病之间的关联信息，选择乳腺癌作为测试示例。结果验证了前50个预测的与乳腺癌相关的miRNA中的50个。
