Machine Learning analysis of the human infant gut microbiome identifies influential species in type 1 diabetes

作者：

Highlights：

• A Machine Learning methodology is proposed for the modelling of metagenomic data and the diagnosis of T1D infants.

• A new metagenomic signature was obtained highly correlated with T1D diagnosis.

• Machine Learning algorithms are able to obtain great results with sparse metagenomic data.

摘要

•A Machine Learning methodology is proposed for the modelling of metagenomic data and the diagnosis of T1D infants.•A new metagenomic signature was obtained highly correlated with T1D diagnosis.•Machine Learning algorithms are able to obtain great results with sparse metagenomic data.

论文关键词：Machine Learning,Diabetes,T1D,Microbiota,Metagenomics,Feature selection,Random forest,Generalized Linear Model

论文评审过程：Received 20 November 2020, Revised 21 June 2021, Accepted 20 July 2021, Available online 29 July 2021, Version of Record 2 August 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115648