Leveraging Scheme for Cross-Study Microbiome Machine Learning Prediction and Feature Evaluations Article Swipe
Kuncheng Song
,
Yi‐Hui Zhou
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/bioengineering10020231
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/bioengineering10020231
The microbiota has proved to be one of the critical factors for many diseases, and researchers have been using microbiome data for disease prediction. However, models trained on one independent microbiome study may not be easily applicable to other independent studies due to the high level of variability in microbiome data. In this study, we developed a method for improving the generalizability and interpretability of machine learning models for predicting three different diseases (colorectal cancer, Crohn’s disease, and immunotherapy response) using nine independent microbiome datasets. Our method involves combining a smaller dataset with a larger dataset, and we found that using at least 25% of the target samples in the source data resulted in improved model performance. We determined random forest as our top model and employed feature selection to identify common and important taxa for disease prediction across the different studies. Our results suggest that this leveraging scheme is a promising approach for improving the accuracy and interpretability of machine learning models for predicting diseases based on microbiome data.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/bioengineering10020231
- https://www.mdpi.com/2306-5354/10/2/231/pdf?version=1676387433
- OA Status
- gold
- Cited By
- 4
- References
- 74
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4319756428
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4319756428Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/bioengineering10020231Digital Object Identifier
- Title
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Leveraging Scheme for Cross-Study Microbiome Machine Learning Prediction and Feature EvaluationsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-02-08Full publication date if available
- Authors
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Kuncheng Song, Yi‐Hui ZhouList of authors in order
- Landing page
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https://doi.org/10.3390/bioengineering10020231Publisher landing page
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https://www.mdpi.com/2306-5354/10/2/231/pdf?version=1676387433Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2306-5354/10/2/231/pdf?version=1676387433Direct OA link when available
- Concepts
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Interpretability, Microbiome, Machine learning, Random forest, Artificial intelligence, Feature selection, Generalizability theory, Computer science, Human microbiome, Support vector machine, Feature (linguistics), Data mining, Bioinformatics, Biology, Statistics, Mathematics, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
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2025: 1, 2024: 2, 2023: 1Per-year citation counts (last 5 years)
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74Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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