Over the years, numerous evidences have demonstrated that microbes living in the human body are closely related to human life activities and human diseases. However, traditional biological experiments are time-consuming and expensive, so it has become a research topic in bioinformatics to predict potential microbe-disease associations by adopting computational methods. In this study, a novel calculative method called BPNNHMDA is proposed to identify potential microbe-disease associations. In BPNNHMDA, a novel neural network model is first designed to infer potential microbe-disease associations, its input signal is a matrix of known microbe-disease associations, and its output signal is matrix of potential microbe-disease associations probabilities. And moreover, in the novel neural network model, a new activation function is designed to activate the hidden layer and the output layer based on the hyperbolic tangent function, and its initial connection weights are optimized by adopting Gaussian Interaction Profile kernel (GIP) similarity for microbes, which can improve the training speed of BPNNHMDA efficiently. Finally, in order to verify the performance of our prediction model, different frameworks such as the Leave-One-Out Cross Validation (LOOCV) and $k$ -Fold Cross Validation ( $k$ -Fold CV) are implemented on BPNNHMDA respectively. Simulation results illustrate that BPNNHMDA can achieve reliable AUCs of 0.9242, 0.9127 $\pm$ 0.0009 and 0.8955 $\pm$ 0.0018 in LOOCV, 5-Fold CV and 2-Fold CV separately, which are superior to previous state-of-the-art methods. Furthermore, case studies of inflammatory bowel disease (IBD), asthma and obesity demonstrate that BPNNHMDA has excellent prediction ability in practical applications as well.

中文翻译：

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基于新型反向传播神经网络模型识别微生物与疾病的关联


多年来，大量证据证明，生活在人体内的微生物与人类生命活动和人类疾病密切相关。然而，传统的生物实验耗时且昂贵，因此采用计算方法预测潜在的微生物与疾病关联已成为生物信息学的研究课题。在这项研究中，提出了一种称为 BPNNHMDA 的新型计算方法来识别潜在的微生物与疾病的关联。在BPNNHMDA中，首先设计了一种新颖的神经网络模型来推断潜在的微生物-疾病关联，其输入信号是已知的微生物-疾病关联矩阵，其输出信号是潜在的微生物-疾病关联概率矩阵。此外，在新颖的神经网络模型中，设计了一种基于双曲正切函数的新激活函数来激活隐藏层和输出层，并采用高斯交互轮廓核（GIP）相似度来优化其初始连接权重微生物，可以有效提高BPNNHMDA的训练速度。最后，为了验证我们的预测模型的性能，分别在 BPNNHMDA 上实现了不同的框架，例如留一交叉验证（LOOCV）和 $k$ -Fold 交叉验证（$k$ -Fold CV）。仿真结果表明，BPNNHMDA 在 LOOCV、5-Fold CV 和 2-Fold CV 中分别可以实现可靠的 AUC 0.9242、0.9127 $\pm$ 0.0009 和 0.8955 $\pm$ 0.0018，优于之前的最佳状态。艺术方法。此外，炎症性肠病（IBD）、哮喘和肥胖的案例研究表明，BPNNHMDA在实际应用中也具有出色的预测能力。