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Folly Beach Library
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Wando Mount Pleasant Library
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Village Library
9 a.m. – 6 p.m.
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St. Paul's/Hollywood Library
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Machine learning approaches in microbiome research: challenges and best practices.
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- Author(s): Papoutsoglou, Georgios; Tarazona, Sonia; Lopes, Marta B.; Klammsteiner, Thomas; Ibrahimi, Eliana; Eckenberger, Julia; Novielli, Pierfrancesco; Tonda, Alberto; Simeon, Andrea; Shigdel, Rajesh; Béreux, Stéphane; Vitali, Giacomo; Tangaro, Sabina; Lahti, Leo; Temko, Andriy; Claesson, Marcus J.; Berland, Magali
- Source:
Frontiers in Microbiology; 2023, p01-21, 21p- Subject Terms:
- Source:
- Additional Information
- Abstract: Microbiome data predictive analysis within a machine learning (ML) workflow presents numerous domain-specific challenges involving preprocessing, feature selection, predictive modeling, performance estimation, model interpretation, and the extraction of biological information from the results. To assist decision-making, we offer a set of recommendations on algorithm selection, pipeline creation and evaluation, stemming from the COST Action ML4Microbiome. We compared the suggested approaches on a multi-cohort shotgun metagenomics dataset of colorectal cancer patients, focusing on their performance in disease diagnosis and biomarker discovery. It is demonstrated that the use of compositional transformations and filtering methods as part of data preprocessing does not always improve the predictive performance of a model. In contrast, the multivariate feature selection, such as the Statistically Equivalent Signatures algorithm, was effective in reducing the classification error. When validated on a separate test dataset, this algorithm in combination with random forest modeling, provided the most accurate performance estimates. Lastly, we showed how linear modeling by logistic regression coupled with visualization techniques such as Individual Conditional Expectation (ICE) plots can yield interpretable results and offer biological insights. These findings are significant for clinicians and nonexperts alike in translational applications. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Frontiers in Microbiology is the property of Frontiers Media S.A. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Abstract:
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