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Communication Dans Un Congrès Année : 2022

Towards a reduced feature selection pipeline in 16s rRNA microbiome data using Machine Learning

Résumé

The use of machine learning (ML) in the analysis of microbiome data is becoming more common due to the ability to process high dimensional data with small number of samples [8]. There are several pipelines for microbiome data analysis that allow researchers to perform statistical analysis to identify representative sequences related to a disease. Once these sequences are identified, researchers apply ML techniques to differentiate between groups, e.g. between healthy controls and patients [6]. Nevertheless, it has been shown that working with a small number of samples can cause overfitting. A proven solution to avoid overfitting is to use nested cross-validation [8] to produce robust and unbiased results regarding the number of samples. In this work, we propose a novel pipeline that combines DADA2 [2], which is an open-source package for modeling and correcting errors in Illumina-sequenced amplicon inferring sample sequences, and a Recursive Ensemble Feature Selection (REFS) [3,4], method to discover biomarkers, applied to 16s rRNA sequencing gut microbiome dataset PRJEB33711 [1]. The aim of the selected study [1] is to show that the gut microbiome of Inflammatory Bowel Disease (IBD) patients is less diverse compared to healthy individuals.
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Dates et versions

hal-04097623 , version 1 (15-05-2023)

Identifiants

  • HAL Id : hal-04097623 , version 1

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David Rojas-Velazquez, Sarah Kidwai, Paula Perez-Pardo, Daniel Oberski, Johan Garssen, et al.. Towards a reduced feature selection pipeline in 16s rRNA microbiome data using Machine Learning. The 9th Beneficial Microbes Conference, Nov 2022, Amsterdam, Netherlands. ⟨hal-04097623⟩
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