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Towards multi-label classification: Next step of machine learning for microbiome research
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,...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Research Network of Computational and Structural Biotechnology
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8131981/ https://www.ncbi.nlm.nih.gov/pubmed/34093989 http://dx.doi.org/10.1016/j.csbj.2021.04.054 |
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author | Wu, Shunyao Chen, Yuzhu Li, Zhiruo Li, Jian Zhao, Fengyang Su, Xiaoquan |
author_facet | Wu, Shunyao Chen, Yuzhu Li, Zhiruo Li, Jian Zhao, Fengyang Su, Xiaoquan |
author_sort | Wu, Shunyao |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8131981 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Research Network of Computational and Structural Biotechnology |
record_format | MEDLINE/PubMed |
spelling | pubmed-81319812021-06-03 Towards multi-label classification: Next step of machine learning for microbiome research Wu, Shunyao Chen, Yuzhu Li, Zhiruo Li, Jian Zhao, Fengyang Su, Xiaoquan Comput Struct Biotechnol J Review Article 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. Research Network of Computational and Structural Biotechnology 2021-04-28 /pmc/articles/PMC8131981/ /pubmed/34093989 http://dx.doi.org/10.1016/j.csbj.2021.04.054 Text en © 2021 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Review Article Wu, Shunyao Chen, Yuzhu Li, Zhiruo Li, Jian Zhao, Fengyang Su, Xiaoquan Towards multi-label classification: Next step of machine learning for microbiome research |
title | Towards multi-label classification: Next step of machine learning for microbiome research |
title_full | Towards multi-label classification: Next step of machine learning for microbiome research |
title_fullStr | Towards multi-label classification: Next step of machine learning for microbiome research |
title_full_unstemmed | Towards multi-label classification: Next step of machine learning for microbiome research |
title_short | Towards multi-label classification: Next step of machine learning for microbiome research |
title_sort | towards multi-label classification: next step of machine learning for microbiome research |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8131981/ https://www.ncbi.nlm.nih.gov/pubmed/34093989 http://dx.doi.org/10.1016/j.csbj.2021.04.054 |
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