4.7 Article

Tracing human life trajectory using gut microbial communities by context-aware deep learning

期刊

BRIEFINGS IN BIOINFORMATICS
卷 24, 期 1, 页码 -

出版社

OXFORD UNIV PRESS
DOI: 10.1093/bib/bbac629

关键词

gut microbial communities; life trajectory; transfer learning; knowledge discovery

向作者/读者索取更多资源

The gut microbial communities are highly plastic throughout life, but the association between gut microbial communities and time-related factors is unclear. In this study, a novel computational approach called microDELTA was used to track longitudinal human gut microbial communities and demonstrated accurate predictions of infant age and the influence of delivery mode. The application of microDELTA to different cohorts revealed the connections between dietary shifts, seasonal variations, and aging on gut microbial communities.
The gut microbial communities are highly plastic throughout life, and the human gut microbial communities show spatial-temporal dynamic patterns at different life stages. However, the underlying association between gut microbial communities and time-related factors remains unclear. The lack of context-awareness, insufficient data, and the existence of batch effect are the three major issues, making the life trajection of the host based on gut microbial communities problematic. Here, we used a novel computational approach (microDELTA, microbial-based deep life trajectory) to track longitudinal human gut microbial communities' alterations, which employs transfer learning for context-aware mining of gut microbial community dynamics at different life stages. Using an infant cohort, we demonstrated that microDELTA outperformed Neural Network for accurately predicting the age of infant with different delivery mode, especially for newborn infants of vaginal delivery with the area under the receiver operating characteristic curve of microDELTA and Neural Network at 0.811 and 0.436, respectively. In this context, we have discovered the influence of delivery mode on infant gut microbial communities. Along the human lifespan, we also applied microDELTA to a Chinese traveler cohort, a Hadza hunter-gatherer cohort and an elderly cohort. Results revealed the association between long-term dietary shifts during travel and adult gut microbial communities, the seasonal cycling of gut microbial communities for the Hadza hunter-gatherers, and the distinctive microbial pattern of elderly gut microbial communities. In summary, microDELTA can largely solve the issues in tracing the life trajectory of the human microbial communities and generate accurate and flexible models for a broad spectrum of microbial-based longitudinal researches.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据