Predicting Microbiome Growth Dynamics under Environmental Perturbations

George Sun, Yi-Hui Zhou
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Abstract

MicroGrowthPredictor is a model that leverages Long Short-Term Memory (LSTM) networks to predict dynamic changes in microbiome growth in response to varying environmental perturbations. In this article, we present the innovative capabilities of MicroGrowthPredictor, which include the integration of LSTM modeling with a novel confidence interval estimation technique. The LSTM network captures the complex temporal dynamics of microbiome systems, while the novel confidence intervals provide a robust measure of prediction uncertainty. We include two examples—one illustrating the human gut microbiota composition and diversity due to recurrent antibiotic treatment and the other demonstrating the application of MicroGrowthPredictor on an artificial gut dataset. The results demonstrate the enhanced accuracy and reliability of the LSTM-based predictions facilitated by MicroGrowthPredictor. The inclusion of specific metrics, such as the mean square error, validates the model’s predictive performance. Our model holds immense potential for applications in environmental sciences, healthcare, and biotechnology, fostering advancements in microbiome research and analysis. Moreover, it is noteworthy that MicroGrowthPredictor is applicable to real data with small sample sizes and temporal observations under environmental perturbations, thus ensuring its practical utility across various domains.
预测环境干扰下微生物群的生长动态
MicroGrowthPredictor 是一种利用长短期记忆(LSTM)网络预测微生物群生长动态变化以应对不同环境扰动的模型。在本文中,我们介绍了 MicroGrowthPredictor 的创新功能,其中包括 LSTM 建模与新型置信区间估计技术的整合。LSTM 网络捕捉了微生物组系统复杂的时间动态,而新型置信区间则提供了预测不确定性的稳健衡量标准。我们列举了两个例子--一个说明了反复抗生素治疗导致的人类肠道微生物群组成和多样性,另一个演示了 MicroGrowthPredictor 在人工肠道数据集上的应用。结果表明,MicroGrowthPredictor 提高了基于 LSTM 预测的准确性和可靠性。均方误差等特定指标的加入验证了模型的预测性能。我们的模型在环境科学、医疗保健和生物技术领域的应用潜力巨大,促进了微生物组研究和分析的进步。此外,值得注意的是,MicroGrowthPredictor 适用于小样本量的真实数据和环境扰动下的时间观测,从而确保了其在各个领域的实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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