IEEE/ACM Trans Comput Biol Bioinform. 2021 Nov-Dec;18(6):2502-2513. doi: 10.1109/TCBB.2020.2986459. Epub 2021 Dec 8.
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 ± 0.0009 and 0.8955 ± 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.
多年来，大量证据表明，生活在人体中的微生物与人类的生命活动和人类疾病密切相关。然而，传统的生物学实验既耗时又昂贵，因此，采用计算方法预测潜在的微生物-疾病关联已成为生物信息学的研究课题。在本研究中，提出了一种新的计算方法 BPNNHMDA 来识别潜在的微生物-疾病关联。在 BPNNHMDA 中，首先设计了一种新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，输出信号是潜在的微生物-疾病关联概率矩阵。此外，在新的神经网络模型中，设计了一种新的激活函数，基于双曲正切函数激活隐藏层和输出层，其初始连接权重通过采用高斯互作用特征核（GIP）相似性优化微生物，这可以有效地提高 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 上实现了留一法交叉验证（LOOCV）和 k 折交叉验证（ k-Fold CV）等不同框架。模拟结果表明，BPNNHMDA 在 LOOCV、5 折 CV 和 2 折 CV 中分别可以达到可靠的 AUC 为 0.9242、0.9127±0.0009 和 0.8955±0.0018，优于先前的最先进方法。此外，炎症性肠病（IBD）、哮喘和肥胖症的案例研究表明，BPNNHMDA 在实际应用中也具有出色的预测能力。
