A Framework for Effective Application of Machine Learning to Microbiome-Based Classification Problems.

ABSTRACT: Machine learning (ML) modeling of the human microbiome has the potential to identify microbial biomarkers and aid in the diagnosis of many diseases such as inflammatory bowel disease, diabetes, and colorectal cancer. Progress has been made toward developing ML models that predict health outcomes using bacterial abundances, but inconsistent adoption of training and evaluation methods call the validity of these models into question. Furthermore, there appears to be a preference by many researchers to favor increased model complexity over interpretability. To overcome these challenges, we trained seven models that used fecal 16S rRNA sequence data to predict the presence of colonic screen relevant neoplasias (SRNs) (n = 490 patients, 261 controls and 229 cases). We developed a reusable

SUBMITTER: Topcuoglu BD 

PROVIDER: S-EPMC7373189 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature