Identifying the microbe-disease associations is conducive to understanding the pathogenesis of disease from the perspective of microbe. In this paper, we propose a deep matrix factorization prediction model (DMFMDA) based on deep neural network. First, the disease one-hot encoding is fed into neural network, which is transformed into a low-dimensional dense vector in implicit semantic space via embedding layer, and so is microbe. Then, matrix factorization is realized by neural network with embedding layer. Furthermore, our model synthesizes the non-linear modeling advantages of multi-layer perceptron based on the linear modeling advantages of matrix factorization. Finally, different from other methods using square error loss function, Bayesian Personalized Ranking optimizes the model from a ranking perspective to obtain the optimal model parameters, which makes full use of the unobserved data. Experiments show that DMFMDA reaches average AUCs of 0.9091 and 0.9103 in the framework of 5-fold cross validation and Leave-one-out cross validation, which is superior to three the-state-of-art methods. In case studies, 10, 9 and 9 out of top-10 candidate microbes are verified by recently published literature for asthma, inflammatory bowel disease and colon cancer, respectively. In conclusion, DMFMDA is successful application of deep learning in the prediction of microbe-disease association.

中文翻译：

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DMFMDA：使用贝叶斯个性化排名基于深度矩阵分解的微生物-疾病关联预测

识别微生物与疾病的关联有助于从微生物的角度理解疾病的发病机制。在本文中，我们提出了一种基于深度神经网络的深度矩阵分解预测模型（DMFMDA）。首先，将疾病one-hot编码输入神经网络，通过嵌入层将其转化为隐语义空间中的低维密集向量，微生物也是如此。然后，通过带有嵌入层的神经网络实现矩阵分解。此外，我们的模型在矩阵分解的线性建模优势的基础上，综合了多层感知器的非线性建模优势。最后，与其他使用平方误差损失函数的方法不同，贝叶斯个性化排名从排名的角度对模型进行优化，得到最优的模型参数，充分利用了未观察到的数据。实验表明，DMFMDA 在 5 折交叉验证和 Leave-one-out 交叉验证的框架下达到了 0.9091 和 0.9103 的平均 AUC，优于三种最先进的方法。在案例研究中，最近发表的哮喘、炎症性肠病和结肠癌文献分别证实了前 10 种候选微生物中的 10 种、9 种和 9 种。总之，DMFMDA 是深度学习在微生物-疾病关联预测中的成功应用。这优于三种最先进的方法。在案例研究中，最近发表的哮喘、炎症性肠病和结肠癌文献分别证实了前 10 种候选微生物中的 10 种、9 种和 9 种。总之，DMFMDA 是深度学习在微生物-疾病关联预测中的成功应用。这优于三种最先进的方法。在案例研究中，最近发表的哮喘、炎症性肠病和结肠癌文献分别证实了前 10 种候选微生物中的 10 种、9 种和 9 种。总之，DMFMDA 是深度学习在微生物-疾病关联预测中的成功应用。