Machine Learning Algorithms Reveals Country-Specific Metagenomic Taxa from American Gut Project Data.

Journal: Studies in health technology and informatics
Published Date:

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.

Authors

  • Jose Liñares-Blanco
    Department of Computer Science and Information Technologies, Faculty of Computer Science, University of A Coruña, CITIC, Campus Elviña s/n, A Coruña, 15071, Spain.
  • Carlos Fernandez-Lozano
    Department of Computer Science and Information Technologies, Faculty of Computer Science, CITIC-Research Center of Information and Communication Technologies, Universidade da Coruña, A Coruña, Spain.
  • José A Seoane
    Bristol Genetic Epidemiology Laboratories, School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS82BN, UK. Electronic address: j.seoane@bristol.ac.uk.
  • Guillermo Lopez-Campos
    Wellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast.