Xu Junlin, Cai Lijun, Liao Bo, Zhu Wen, Wang Peng, Meng Yajie, Lang Jidong, Tian Geng, Yang Jialiang
College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.
School of Mathematics and Statistics, Hainan Normal University, Haikou, China.
Front Genet. 2019 Dec 11;10:1234. doi: 10.3389/fgene.2019.01234. eCollection 2019.
In recent years, miRNAs have been verified to play an irreplaceable role in biological processes associated with human disease. Discovering potential disease-related miRNAs helps explain the underlying pathogenesis of the disease at the molecular level. Given the high cost and labor intensity of biological experiments, computational predictions will be an indispensable alternative. Therefore, we design a new model called probability matrix factorization (PMFMDA). Specifically, we first integrate miRNA and disease similarity. Next, the known association matrix and integrated similarity matrix are utilized to construct a probability matrix factorization algorithm to identify potentially relevant miRNAs for disease. We find that PMFMDA achieves reliable performance in the frameworks of global leave-one-out cross validation (LOOCV) and 5-fold cross validation (AUCs are 0.9237 and 0.9187, respectively) in the HMDD (V2.0) dataset, significantly outperforming a few state-of-the-art methods including CMFMDA, IMCMDA, NCPMDA, RLSMDA, and RWRMDA. In addition, case studies show that PMFMDA has good predictive performance for new associations, and the evidence can be identified by literature mining.
近年来，已证实miRNA在与人类疾病相关的生物学过程中发挥着不可替代的作用。发现潜在的疾病相关miRNA有助于在分子水平上解释疾病的潜在发病机制。鉴于生物实验成本高且劳动强度大，计算预测将是不可或缺的替代方法。因此，我们设计了一种名为概率矩阵分解（PMFMDA）的新模型。具体而言，我们首先整合miRNA与疾病的相似性。接下来，利用已知的关联矩阵和整合后的相似性矩阵构建概率矩阵分解算法，以识别与疾病潜在相关的miRNA。我们发现，在HMDD（V2.0）数据集中，PMFMDA在全局留一法交叉验证（LOOCV）和5折交叉验证框架下均取得了可靠的性能（AUC分别为0.9237和0.9187），显著优于包括CMFMDA、IMCMDA、NCPMDA、RLSMDA和RWRMDA在内的一些现有先进方法。此外，案例研究表明，PMFMDA对新关联具有良好的预测性能，且证据可通过文献挖掘来识别。
