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专家:基于迁移学习的上下文感知微生物群落分类。

EXPERT: transfer learning-enabled context-aware microbial community classification.

机构信息

Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular-imaging, Center of AI Biology, Department of Bioinformatics and Systems Biology, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China.

Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China.

出版信息

Brief Bioinform. 2022 Nov 19;23(6). doi: 10.1093/bib/bbac396.

Abstract

Microbial community classification enables identification of putative type and source of the microbial community, thus facilitating a better understanding of how the taxonomic and functional structure were developed and maintained. However, previous classification models required a trade-off between speed and accuracy, and faced difficulties to be customized for a variety of contexts, especially less studied contexts. Here, we introduced EXPERT based on transfer learning that enabled the classification model to be adaptable in multiple contexts, with both high efficiency and accuracy. More importantly, we demonstrated that transfer learning can facilitate microbial community classification in diverse contexts, such as classification of microbial communities for multiple diseases with limited number of samples, as well as prediction of the changes in gut microbiome across successive stages of colorectal cancer. Broadly, EXPERT enables accurate and context-aware customized microbial community classification, and potentiates novel microbial knowledge discovery.

摘要

微生物群落分类能够识别微生物群落的假定类型和来源,从而有助于更好地了解分类和功能结构是如何发展和维持的。然而,以前的分类模型在速度和准确性之间需要进行权衡,并且难以针对各种上下文(特别是研究较少的上下文)进行定制。在这里,我们介绍了基于迁移学习的 EXPERT,它使分类模型能够在多个上下文中具有适应性,同时具有高效率和准确性。更重要的是,我们证明了迁移学习可以促进不同背景下的微生物群落分类,例如,对具有有限样本量的多种疾病的微生物群落进行分类,以及对结直肠癌连续阶段的肠道微生物组变化进行预测。广义而言,EXPERT 能够实现准确且具有上下文感知能力的定制微生物群落分类,并促进新的微生物知识发现。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8bf8/9677468/b606d6ea5592/bbac396f1.jpg

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