Weng Qinghui, Hu Mingyi, Peng Guohao, Zhu Jinlin
The State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi, 214122, Jiangsu, People's Republic of China.
The School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, 214122, Jiangsu, People's Republic of China.
BMC Bioinformatics. 2025 Mar 27;26(1):93. doi: 10.1186/s12859-025-06110-7.
Understanding the metabolic activities of the gut microbiome is vital for deciphering its impact on human health. While direct measurement of these metabolites through metabolomics is effective, it is often expensive and time-consuming. In contrast, microbial composition data obtained through sequencing is more accessible, making it a promising resource for predicting metabolite profiles. However, current computational models frequently face challenges related to limited prediction accuracy, generalizability, and interpretability.
Here, we present the Deep Mixture of Variational Gaussian Process Experts (DMoVGPE) model, designed to overcome these issues. DMoVGPE utilizes a dynamic gating mechanism, implemented through a neural network with fully connected layers and dropout for regularization, to select the most relevant Gaussian Process experts. During training, the gating network refines expert selection, dynamically adjusting their contribution based on the input features. The model also incorporates an Automatic Relevance Determination (ARD) mechanism, which assigns relevance scores to microbial features by evaluating their predictive power. Features linked to metabolite profiles are given smaller length scales to increase their influence, while irrelevant features are down-weighted through larger length scales, improving both prediction accuracy and interpretability.
Through extensive evaluations on various datasets, DMoVGPE consistently achieves higher prediction performance than existing models. Furthermore, our model reveals significant associations between specific microbial taxa and metabolites, aligning well with findings from existing studies. These results highlight DMoVGPE's potential to provide accurate predictions and to uncover biologically meaningful relationships, paving the way for its application in disease research and personalized healthcare strategies.
了解肠道微生物群的代谢活动对于解读其对人类健康的影响至关重要。虽然通过代谢组学直接测量这些代谢物是有效的，但通常成本高昂且耗时。相比之下，通过测序获得的微生物组成数据更容易获取，使其成为预测代谢物谱的有前景的资源。然而，当前的计算模型经常面临与预测准确性、泛化性和可解释性有限相关的挑战。
在此，我们提出了深度变分高斯过程专家混合模型（DMoVGPE），旨在克服这些问题。DMoVGPE利用一种动态门控机制，通过具有全连接层和用于正则化的随机失活的神经网络来实现，以选择最相关的高斯过程专家。在训练期间，门控网络优化专家选择，根据输入特征动态调整它们的贡献。该模型还纳入了自动相关性确定（ARD）机制，通过评估微生物特征的预测能力为其分配相关性分数。与代谢物谱相关的特征被赋予较小的长度尺度以增加其影响，而不相关的特征则通过较大的长度尺度进行加权下调，从而提高预测准确性和可解释性。
通过对各种数据集的广泛评估，DMoVGPE始终比现有模型取得更高的预测性能。此外，我们的模型揭示了特定微生物分类群与代谢物之间的显著关联，与现有研究结果高度一致。这些结果凸显了DMoVGPE在提供准确预测和揭示生物学上有意义的关系方面的潜力，为其在疾病研究和个性化医疗策略中的应用铺平了道路。
