Machine Learning Algorithms Reveals Country-Specific Metagenomic Taxa from American Gut Project Data
Use this link to cite
http://hdl.handle.net/2183/28413
Except where otherwise noted, this item's license is described as Atribución-NoComercial 4.0 International (CC BY-NC 4.0)
Collections
- GI-RNASA - Artigos [195]
Metadata
Show full item recordTitle
Machine Learning Algorithms Reveals Country-Specific Metagenomic Taxa from American Gut Project DataAuthor(s)
Date
2021Citation
Liñares-Blanco J, Fernandez-Lozano C, Seoane JA, Lopez-Campos G. Machine Learning Algorithms Reveals Country-Specific Metagenomic Taxa from American Gut Project Data. Studies in Health Technology and Informatics. 2021 May;281:382-386. DOI: 10.3233/shti210185. PMID: 34042770.
Abstract
[Abstract] In recent years, microbiota has become an increasingly relevant factor for the understanding and potential treatment of diseases. In this work, based on the data reported by the largest study of microbioma in the world, a classification model has been developed based on Machine Learning (ML) capable of predicting the country of origin (United Kingdom vs United States) according to metagenomic data. The data were used for the training of a glmnet algorithm and a Random Forest algorithm. Both algorithms obtained similar results (0.698 and 0.672 in AUC, respectively). Furthermore, thanks to the application of a multivariate feature selection algorithm, eleven metagenomic genres highly correlated with the country of origin were obtained. An in-depth study of the variables used in each model is shown in the present work.
Keywords
Feature selection
Machine-learning
Metagenomics
Machine-learning
Metagenomics
Editor version
Rights
Atribución-NoComercial 4.0 International (CC BY-NC 4.0)