Qu Jia, Zhao Yan, Yin Jun
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China.
Front Microbiol. 2019 Feb 26;10:291. doi: 10.3389/fmicb.2019.00291. eCollection 2019.
Studies have shown that microbes exist widely in the human body and are closely related to human complex diseases. Predicting potential associations between microbes and diseases is conducive to understanding the mechanisms of complex diseases and can also facilitate the diagnosis and prevention of human diseases. In this paper, we put forward the Matrix Decomposition and Label Propagation for Human Microbe-Disease Association prediction (MDLPHMDA) on the basis of the dataset of known microbe-disease associations collected from the database of HMDAD and the Gaussian interaction profile kernel similarity for diseases and microbes, disease symptom similarity. Moreover, the performance of our model was evaluated by means of leave-one-out cross validation and five-fold cross validation, and the corresponding AUCs of 0.9034 and 0.8954 ± 0.0030 were gained, respectively. In case studies, 10, 9, 9, and 8 out of the top 10 predicted microbes for asthma, colorectal carcinoma, liver cirrhosis, and type 1 diabetes were confirmed by literatures, respectively. Overall, evaluation results showed that MDLPHMDA has good performance in potential microbe-diseasepositive free parameter, which associations prediction.
研究表明，微生物广泛存在于人体中，且与人类复杂疾病密切相关。预测微生物与疾病之间的潜在关联有助于理解复杂疾病的发病机制，还能促进人类疾病的诊断和预防。在本文中，我们基于从人类微生物-疾病关联数据库（HMDAD）收集的已知微生物-疾病关联数据集、疾病和微生物的高斯相互作用轮廓核相似性以及疾病症状相似性，提出了用于人类微生物-疾病关联预测的矩阵分解与标签传播方法（MDLPHMDA）。此外，我们通过留一法交叉验证和五折交叉验证对模型性能进行了评估，分别获得了0.9034和0.8954±0.0030的相应曲线下面积（AUC）。在案例研究中，哮喘、结直肠癌、肝硬化和1型糖尿病预测排名前10的微生物中，分别有10种、9种、9种和8种被文献证实。总体而言，评估结果表明MDLPHMDA在潜在微生物-疾病关联预测方面具有良好的性能。
