College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China.
Genes (Basel). 2019 Aug 12;10(8):608. doi: 10.3390/genes10080608.
Identifying associations between lncRNAs and diseases can help understand disease-related lncRNAs and facilitate disease diagnosis and treatment. The dual-network integrated logistic matrix factorization (DNILMF) model has been used for drug-target interaction prediction, and good results have been achieved. We firstly applied DNILMF to lncRNA-disease association prediction (DNILMF-LDA). We combined different similarity kernel matrices of lncRNAs and diseases by using nonlinear fusion to extract the most important information in fused matrices. Then, lncRNA-disease association networks and similarity networks were built simultaneously. Finally, the Gaussian process mutual information (GP-MI) algorithm of Bayesian optimization was adopted to optimize the model parameters. The 10-fold cross-validation result showed that the area under receiving operating characteristic (ROC) curve (AUC) value of DNILMF-LDA was 0.9202, and the area under precision-recall (PR) curve (AUPR) was 0.5610. Compared with LRLSLDA, SIMCLDA, BiwalkLDA, and TPGLDA, the AUC value of our method increased by 38.81%, 13.07%, 8.35%, and 6.75%, respectively. The AUPR value of our method increased by 52.66%, 40.05%, 37.01%, and 44.25%. These results indicate that DNILMF-LDA is an effective method for predicting the associations between lncRNAs and diseases.
鉴定 lncRNA 和疾病之间的关联有助于理解与疾病相关的 lncRNA，并促进疾病的诊断和治疗。双网络集成逻辑矩阵分解 (DNILMF) 模型已被用于药物-靶标相互作用预测，并取得了良好的效果。我们首次将 DNILMF 应用于 lncRNA-疾病关联预测 (DNILMF-LDA)。我们通过使用非线性融合，将 lncRNA 和疾病的不同相似性核矩阵结合起来，以提取融合矩阵中最重要的信息。然后，同时构建 lncRNA-疾病关联网络和相似性网络。最后，采用贝叶斯优化的高斯过程互信息 (GP-MI) 算法优化模型参数。10 倍交叉验证结果表明，DNILMF-LDA 的接收操作特征 (ROC) 曲线下面积 (AUC) 值为 0.9202，精度-召回率 (PR) 曲线下面积 (AUPR) 值为 0.5610。与 LRLSLDA、SIMCLDA、BiwalkLDA 和 TPGLDA 相比，我们的方法的 AUC 值分别增加了 38.81%、13.07%、8.35%和 6.75%，AUPR 值分别增加了 52.66%、40.05%、37.01%和 44.25%。这些结果表明，DNILMF-LDA 是一种预测 lncRNA 和疾病之间关联的有效方法。
