College of Big Data Statistics, Guizhou University of Finance and Economics, Guiyang, China.
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.
J Cell Mol Med. 2020 Jan;24(1):573-587. doi: 10.1111/jcmm.14765. Epub 2019 Nov 20.
Accumulating experimental evidence has demonstrated that microRNAs (miRNAs) have a huge impact on numerous critical biological processes and they are associated with different complex human diseases. Nevertheless, the task to predict potential miRNAs related to diseases remains difficult. In this paper, we developed a Kernel Fusion-based Regularized Least Squares for MiRNA-Disease Association prediction model (KFRLSMDA), which applied kernel fusion technique to fuse similarity matrices and then utilized regularized least squares to predict potential miRNA-disease associations. To prove the effectiveness of KFRLSMDA, we adopted leave-one-out cross-validation (LOOCV) and 5-fold cross-validation and then compared KFRLSMDA with 10 previous computational models (MaxFlow, MiRAI, MIDP, RKNNMDA, MCMDA, HGIMDA, RLSMDA, HDMP, WBSMDA and RWRMDA). Outperforming other models, KFRLSMDA achieved AUCs of 0.9246 in global LOOCV, 0.8243 in local LOOCV and average AUC of 0.9175 ± 0.0008 in 5-fold cross-validation. In addition, respectively, 96%, 100% and 90% of the top 50 potential miRNAs for breast neoplasms, colon neoplasms and oesophageal neoplasms were confirmed by experimental discoveries. We also predicted potential miRNAs related to hepatocellular cancer by removing all known related miRNAs of this cancer and 98% of the top 50 potential miRNAs were verified. Furthermore, we predicted potential miRNAs related to lymphoma using the data set in the old version of the HMDD database and 80% of the top 50 potential miRNAs were confirmed. Therefore, it can be concluded that KFRLSMDA has reliable prediction performance.
越来越多的实验证据表明，微小 RNA（miRNA）对许多关键的生物过程有巨大影响，并且与不同的复杂人类疾病有关。然而，预测与疾病相关的潜在 miRNA 的任务仍然具有挑战性。在本文中，我们开发了一种基于核融合正则化最小二乘的 miRNA-疾病关联预测模型（KFRLSMDA），该模型应用核融合技术融合相似性矩阵，然后利用正则化最小二乘法预测潜在的 miRNA-疾病关联。为了证明 KFRLSMDA 的有效性，我们采用了留一法交叉验证（LOOCV）和 5 折交叉验证，并将 KFRLSMDA 与 10 种先前的计算模型（MaxFlow、MiRAI、MIDP、RKNNMDA、MCMDA、HGIMDA、RLSMDA、HDMP、WBSMDA 和 RWRMDA）进行了比较。在全局 LOOCV 中，KFRLSMDA 的 AUC 为 0.9246，在局部 LOOCV 中，AUC 为 0.8243，在 5 折交叉验证中的平均 AUC 为 0.9175±0.0008，表现优于其他模型。此外，在乳腺癌、结肠癌和食管癌的前 50 个潜在 miRNA 中，分别有 96%、100%和 90%被实验发现所证实。我们还通过去除这种癌症的所有已知相关 miRNA 来预测与肝癌相关的潜在 miRNA，其中前 50 个潜在 miRNA 的 98%得到了验证。此外，我们使用 HMDD 数据库旧版本中的数据集预测与淋巴瘤相关的潜在 miRNA，其中前 50 个潜在 miRNA 的 80%得到了验证。因此，可以得出结论，KFRLSMDA 具有可靠的预测性能。
