文章基本信息

标题：Towards multi-label classification: Next step of machine learning for microbiome research
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作者：Shunyao Wu ; Yuzhu Chen ; Zhiruo Li 等
期刊名称：Computational and Structural Biotechnology Journal
印刷版ISSN：2001-0370
出版年度：2021
卷号：19
页码：2742-2749
DOI：10.1016/j.csbj.2021.04.054
出版社：Computational and Structural Biotechnology Journal
摘要：Machine learning (ML) has been widely used in microbiome research for biomarker selection and disease prediction. By training microbial profiles of samples from patients and healthy controls, ML classifiers constructs data models by community features that highly correlated with the target diseases, so as to determine the status of new samples. To clearly understand the host-microbe interaction of specific diseases, previous studies always focused on well-designed cohorts, in which each sample was exactly labeled by a single status type. However, in fact an individual may be associated with multiple diseases simultaneously, which introduce additional variations on microbial patterns that interferes the status detection. More importantly, comorbidities or complications can be missed by regular ML models, limiting the practical application of microbiome techniques. In this review, we summarize the typical ML approaches of single-label classification for microbiome research, and demonstrate their limitations in multi-label disease detection using a real dataset. Then we prospect a further step of ML towards multi-label classification that potentially solves the aforementioned problem, including a series of promising strategies and key technical issues for applying multi-label classification in microbiome-based studies.
关键词：Machine learning ; Microbiome ; Single-label classification ; Multi-label classification