DMoVGPE:预测肠道微生物相关代谢物剖面与变分高斯过程专家的深度混合。

IF 2.9 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Qinghui Weng, Mingyi Hu, Guohao Peng, Jinlin Zhu
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引用次数: 0

摘要

背景: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,但它通常是昂贵和耗时的。相比之下,通过测序获得的微生物组成数据更容易获得,使其成为预测代谢物谱的有前途的资源。然而,当前的计算模型经常面临与有限的预测精度、概括性和可解释性相关的挑战。方法:在这里,我们提出了深度混合变分高斯过程专家(DMoVGPE)模型,旨在克服这些问题。DMoVGPE利用动态门控机制,通过具有完全连接层和正则化dropout的神经网络实现,选择最相关的高斯过程专家。在训练过程中,门控网络细化专家选择,根据输入特征动态调整专家贡献。该模型还结合了自动相关性确定(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降权重,从而提高预测准确性和可解释性。结论:通过对各种数据集的广泛评估,DMoVGPE始终具有比现有模型更高的预测性能。此外,我们的模型揭示了特定微生物分类群和代谢物之间的显著关联,与现有研究的结果很好地一致。这些结果突出了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保健策略中的应用铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DMoVGPE: predicting gut microbial associated metabolites profiles with deep mixture of variational Gaussian Process experts.

Background: 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.

Method: 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.

Conclusions: 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.

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来源期刊
BMC Bioinformatics
BMC Bioinformatics 生物-生化研究方法
CiteScore
5.70
自引率
3.30%
发文量
506
审稿时长
4.3 months
期刊介绍: BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology. BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.
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