School of Information and Control Engineering , China University of Mining and Technology , Xuzhou 221116 , China.
J Chem Inf Model. 2019 Dec 23;59(12):5281-5293. doi: 10.1021/acs.jcim.9b00667. Epub 2019 Dec 12.
MicroRNAs (miRNAs) play a key role in many critical biological processes and are involved in the occurrence and development of complex human diseases. Many studies demonstrated that discovering the associations between small molecules (SMs) and miRNAs will facilitate the design of miRNA targeted therapeutic strategies for complex human diseases. This work presents a calculation model of cross-layer dependency inference on multilayered networks for small molecule-miRNA association prediction (CLDISMMA), which constructed multilayered networks composed of SMs, miRNAs, and diseases. It utilized the within layer topology and the known cross-layer associations to infer latent representations of all layers for SM-miRNA association prediction. In CLDISMMA, the novelties lie in introducing disease information for SM-miRNA association prediction and utilizing a regularized optimization model to describe the SM-miRNA association prediction problem. To evaluate the performance of CLDISMMA, global leave-one-out cross validation (LOOCV) and miRNA-fixed and SM-fixed local LOOCV were implemented in two data sets. In data set 1, CLDISMMA achieved AUCs of 0.9889, 0.9886, and 0.7755 in turns. The corresponding AUCs were 0.8726, 0.8798, and 0.7021 based on data set 2. In addition, CLDISMMA obtained average AUCs of 0.9887 and 0.8647 in data sets 1 and 2 under 100 times 5-fold cross validation. Furthermore, we employed CLDISMMA to predict SM-miRNA associations based on data set 1, and 21 out of the top 50 predicted associations were confirmed by experimental reports. In the case study for new SMs, 5-fluorouracil and 5-aza-2'-deoxycytidine, 40 and 30 miRNAs, respectively, were verified to be associated with them among the top 50 miRNAs predicted by CLDISMMA.
微小 RNA（miRNA）在许多关键的生物过程中发挥着关键作用，并参与复杂人类疾病的发生和发展。许多研究表明，发现小分子（SM）和 miRNA 之间的关联将有助于设计针对复杂人类疾病的 miRNA 靶向治疗策略。本工作提出了一种用于小分子-miRNA 关联预测的多层次网络的跨层依赖推断计算模型（CLDISMMA），该模型构建了由小分子、miRNA 和疾病组成的多层次网络。它利用层内拓扑结构和已知的跨层关联来推断所有层的潜在表示，以进行 SM-miRNA 关联预测。在 CLDISMMA 中，新颖之处在于引入疾病信息进行 SM-miRNA 关联预测，并利用正则化优化模型来描述 SM-miRNA 关联预测问题。为了评估 CLDISMMA 的性能，在两个数据集上进行了全局留一法交叉验证（LOOCV）和 miRNA 固定和 SM 固定局部 LOOCV。在数据集 1 中，CLDISMMA 的 AUC 分别为 0.9889、0.9886 和 0.7755。在数据集 2 中，相应的 AUC 分别为 0.8726、0.8798 和 0.7021。此外，在数据集 1 和 2 上进行的 100 次 5 倍交叉验证中，CLDISMMA 分别获得了平均 AUC 为 0.9887 和 0.8647。此外，我们使用 CLDISMMA 根据数据集 1 预测 SM-miRNA 关联，在 top 50 预测关联中，有 21 个被实验报道证实。在新 SMs 的案例研究中，分别有 5-氟尿嘧啶和 5-氮杂-2'-脱氧胞苷的 40 个和 30 个 miRNA 被 CLDISMMA 预测为与其相关的 top 50 miRNA 之一。
