Incorporating metabolic activity, taxonomy and community structure to improve microbiome-based predictive models for host phenotype prediction.

IF 12.2 1区 医学 Q1 GASTROENTEROLOGY & HEPATOLOGY Gut Microbes Pub Date : 2024-01-01 Epub Date: 2024-01-12 DOI:10.1080/19490976.2024.2302076
Mahsa Monshizadeh, Yuzhen Ye
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Abstract

We developed MicroKPNN, a prior-knowledge guided interpretable neural network for microbiome-based human host phenotype prediction. The prior knowledge used in MicroKPNN includes the metabolic activities of different bacterial species, phylogenetic relationships, and bacterial community structure, all in a shallow neural network. Application of MicroKPNN to seven gut microbiome datasets (involving five different human diseases including inflammatory bowel disease, type 2 diabetes, liver cirrhosis, colorectal cancer, and obesity) shows that incorporation of the prior knowledge helped improve the microbiome-based host phenotype prediction. MicroKPNN outperformed fully connected neural network-based approaches in all seven cases, with the most improvement of accuracy in the prediction of type 2 diabetes. MicroKPNN outperformed a recently developed deep-learning based approach DeepMicro, which selects the best combination of autoencoder and machine learning approach to make predictions, in all of the seven cases. Importantly, we showed that MicroKPNN provides a way for interpretation of the predictive models. Using importance scores estimated for the hidden nodes, MicroKPNN could provide explanations for prior research findings by highlighting the roles of specific microbiome components in phenotype predictions. In addition, it may suggest potential future research directions for studying the impacts of microbiome on host health and diseases. MicroKPNN is publicly available at https://github.com/mgtools/MicroKPNN.

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结合代谢活动、分类和群落结构,改进基于微生物组的宿主表型预测模型。
我们开发了 MicroKPNN,这是一种先验知识指导下的可解释神经网络,用于基于微生物组的人类宿主表型预测。MicroKPNN 使用的先验知识包括不同细菌物种的代谢活动、系统发育关系和细菌群落结构,所有这些都在一个浅层神经网络中。MicroKPNN 在七个肠道微生物组数据集(涉及五种不同的人类疾病,包括炎症性肠病、2 型糖尿病、肝硬化、结直肠癌和肥胖症)中的应用表明,先验知识的加入有助于改善基于微生物组的宿主表型预测。在所有七个案例中,MicroKPNN 的表现都优于基于全连接神经网络的方法,其中对 2 型糖尿病的预测准确率提高最多。MicroKPNN 在所有 7 个案例中的表现都优于最近开发的基于深度学习的 DeepMicro 方法,该方法选择自动编码器和机器学习方法的最佳组合来进行预测。重要的是,我们发现 MicroKPNN 提供了一种解释预测模型的方法。利用为隐藏节点估算的重要性分数,MicroKPNN 可以通过强调特定微生物组成分在表型预测中的作用,为先前的研究结果提供解释。此外,它还可以为研究微生物组对宿主健康和疾病的影响提出潜在的未来研究方向。MicroKPNN 可通过 https://github.com/mgtools/MicroKPNN 公开获取。
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来源期刊
Gut Microbes
Gut Microbes Medicine-Microbiology (medical)
CiteScore
18.20
自引率
3.30%
发文量
196
审稿时长
10 weeks
期刊介绍: The intestinal microbiota plays a crucial role in human physiology, influencing various aspects of health and disease such as nutrition, obesity, brain function, allergic responses, immunity, inflammatory bowel disease, irritable bowel syndrome, cancer development, cardiac disease, liver disease, and more. Gut Microbes serves as a platform for showcasing and discussing state-of-the-art research related to the microorganisms present in the intestine. The journal emphasizes mechanistic and cause-and-effect studies. Additionally, it has a counterpart, Gut Microbes Reports, which places a greater focus on emerging topics and comparative and incremental studies.
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