Improved Key Microbial Biomarker Discovery Using Ensemble Statistical Methods Article Swipe
Walter Pirovano
,
Yashjit Gangopadhyay
,
Mirna L. Baak
,
Christiaan de Leeuw
,
Radhika Bongoni
,
Eline S. Klaassens
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1155/agm3/9676659
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1155/agm3/9676659
In recent years, there has been a growing awareness of the importance of the microbiome in health and disease. Consequently, the number of large microbiome‐related clinical trials has also significantly increased. However, advanced biostatistical analysis is required to properly combine microbiome taxonomic abundance data with phenotypical metadata and reliably predict disease states. While differential abundance analysis and machine‐learning techniques are widely used to perform such analyses, selecting the best method is not trivial due to the complexity and specific characteristics of both the data and the algorithms. Here, we present a consensus‐based key microbial biomarker (KMB) biostatistical analysis framework that links microbial abundance obtained from amplicon‐based or shotgun metagenome sequencing with metadata. The framework integrates machine learning (ML) algorithms and statistical methods to determine the most relevant microbial biomarkers and signatures that explain variation in the microbial abundance counts and metadata classes based on predefined metrics. We evaluated the performance of our framework on publicly available case‐control datasets of colorectal cancer, Alzheimer’s disease, and Parkinson’s disease and show that, compared to individually run methods, the combined approach is better able to detect KMB species and signatures associated with health and disease conditions. We conclude that our proposed KMB framework provides an innovative and robust strategy that can contribute to the further development of improved diagnostic tools for early disease detection, personalized medicine design, patient stratification, and a better general understanding of the mechanisms behind observed results in pre and postclinical trials.
Related Topics
Concepts
Key (lock)
Biomarker discovery
Computational biology
Biomarker
Computer science
Biology
Proteomics
Genetics
Gene
Computer security
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/agm3/9676659
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1155/agm3/9676659
- OA Status
- diamond
- References
- 143
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://doi.org/10.1155/agm3/9676659Digital Object Identifier
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Improved Key Microbial Biomarker Discovery Using Ensemble Statistical MethodsWork title
- Type
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articleOpenAlex work type
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-01Full publication date if available
- Authors
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Walter Pirovano, Yashjit Gangopadhyay, Mirna L. Baak, Christiaan de Leeuw, Radhika Bongoni, Eline S. KlaassensList of authors in order
- Landing page
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https://doi.org/10.1155/agm3/9676659Publisher landing page
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1155/agm3/9676659Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1155/agm3/9676659Direct OA link when available
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Key (lock), Biomarker discovery, Computational biology, Biomarker, Computer science, Biology, Proteomics, Genetics, Gene, Computer securityTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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