School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China.
School of Life Science, Peking University, Beijing, China.
J Cell Mol Med. 2018 Jan;22(1):472-485. doi: 10.1111/jcmm.13336. Epub 2017 Aug 31.
Recently, microRNAs (miRNAs) are confirmed to be important molecules within many crucial biological processes and therefore related to various complex human diseases. However, previous methods of predicting miRNA-disease associations have their own deficiencies. Under this circumstance, we developed a prediction method called deep representations-based miRNA-disease association (DRMDA) prediction. The original miRNA-disease association data were extracted from HDMM database. Meanwhile, stacked auto-encoder, greedy layer-wise unsupervised pre-training algorithm and support vector machine were implemented to predict potential associations. We compared DRMDA with five previous classical prediction models (HGIMDA, RLSMDA, HDMP, WBSMDA and RWRMDA) in global leave-one-out cross-validation (LOOCV), local LOOCV and fivefold cross-validation, respectively. The AUCs achieved by DRMDA were 0.9177, 08339 and 0.9156 ± 0.0006 in the three tests above, respectively. In further case studies, we predicted the top 50 potential miRNAs for colon neoplasms, lymphoma and prostate neoplasms, and 88%, 90% and 86% of the predicted miRNA can be verified by experimental evidence, respectively. In conclusion, DRMDA is a promising prediction method which could identify potential and novel miRNA-disease associations.
最近，microRNAs（miRNAs）被证实是许多重要生物过程中的重要分子，因此与各种复杂的人类疾病有关。然而，先前预测 miRNA-疾病关联的方法存在各自的缺陷。在这种情况下，我们开发了一种称为基于深度表示的 miRNA-疾病关联预测（DRMDA）的预测方法。原始的 miRNA-疾病关联数据从 HDMM 数据库中提取。同时，采用堆叠自动编码器、贪婪逐层无监督预训练算法和支持向量机来预测潜在的关联。我们分别在全局留一法交叉验证（LOOCV）、局部 LOOCV 和五折交叉验证中，将 DRMDA 与五种先前的经典预测模型（HGIMDA、RLSMDA、HDMP、WBSMDA 和 RWRMDA）进行了比较。在上述三种测试中，DRMDA 的 AUC 分别为 0.9177、0.8339 和 0.9156±0.0006。在进一步的案例研究中，我们预测了结肠癌、淋巴瘤和前列腺癌的前 50 个潜在 miRNA，其中 88%、90%和 86%的预测 miRNA 可以通过实验证据验证。总之，DRMDA 是一种很有前途的预测方法，可以识别潜在的和新的 miRNA-疾病关联。
