Predicting gut metabolites from gut microbiome and their interpretability analysis of IBD prediction based on LIME.

IF 1.4
Jing Liu, Kun Li, Yu Zhang, Kai Huang, Xiao Guan
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Abstract

The pathophysiology of inflammatory bowel disease (IBD) is influenced by the gut microbiome and gut metabolite, but understanding how IBD is affected remains challenging. It is crucial to understand which features affect IBD in order to effectively diagnose the disease. Traditional technology for measuring metabolite features is time-consuming and costly. The abundance of metabolite features in IBD patients is altered depending to changes in the abundance of gut microbiome. LSTM-VAE is proposed to predict gut metabolite features using gut microbiome of IBD patients. The pathogenesis of IBD is investigated by LSTM-VAE without gut metabolite data. In order to explore IBD is affected by the features, GBDT-LR is used to predict IBD disease using the gut microbiome and the generated gut metabolites. GBDT-LR achieved high-precision prediction, with an accuracy of 0.97 at the genus level and 0.95 at the species level. It is noteworthy that LIME is used to explain the prediction process of GBDT-LR, solving the prediction of the 'black box model'. The cost of measuring intestinal metabolites were reduced in this study and the researches were assisted in the diagnosis and drug research of IBD diseases. Insight Box Dysbiosis of the gut microbiota and the resulting abnormal metabolites were influenced in the IBD, promoting inflammatory responses and damaging intestinal barrier function. LSTM-VAE was proposed to predict changes in gut metabolite features in IBD patients without the need for direct measurement of costly and time-consuming metabolite data. Furthermore, high-precision prediction of IBD based on gut microbiome data was demonstrated and metabolite features were generated in the GBDT-LR, achieving accuracy rates of 0.97 at the genus level and 0.95 at the species level. Additionally, the LIME is employed to interpret the "black box" prediction process of GBDT-LR. The cost of measuring gut metabolites was reduced, but also strong support for the diagnosis and drug development of IBD was provided.

从肠道微生物组预测肠道代谢物及其基于LIME预测IBD的可解释性分析。
炎症性肠病(IBD)的病理生理受到肠道微生物组和肠道代谢物的影响,但了解IBD如何受到影响仍然具有挑战性。了解哪些特征影响IBD是至关重要的,以便有效地诊断该疾病。测量代谢物特征的传统技术既耗时又昂贵。IBD患者代谢产物特征的丰度取决于肠道微生物群丰度的变化。LSTM-VAE被用来预测IBD患者肠道微生物组的肠道代谢物特征。IBD的发病机制是通过LSTM-VAE研究的,没有肠道代谢物的数据。为了探索IBD受这些特征的影响,我们利用GBDT-LR利用肠道微生物组和产生的肠道代谢物来预测IBD疾病。GBDT-LR具有较高的预测精度,属水平的预测精度为0.97,种水平的预测精度为0.95。值得注意的是,使用LIME来解释GBDT-LR的预测过程,解决了“黑箱模型”的预测问题。本研究降低了肠道代谢物测量的成本,辅助IBD疾病的诊断和药物研究。肠道菌群的生态失调和由此产生的异常代谢物在IBD中受到影响,促进炎症反应并破坏肠道屏障功能。LSTM-VAE被提出用于预测IBD患者肠道代谢物特征的变化,而不需要直接测量昂贵且耗时的代谢物数据。此外,基于肠道微生物组数据的IBD高精度预测得到了证实,并在GBDT-LR中生成了代谢物特征,在属水平上的准确率为0.97,在种水平上的准确率为0.95。此外,利用LIME对GBDT-LR的“黑箱”预测过程进行了解释。降低了测量肠道代谢物的成本,也为IBD的诊断和药物开发提供了强有力的支持。
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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