Increasing prediction performance of colorectal cancer disease status using random forests classification based on metagenomic shotgun sequencing data.

ABSTRACT: Dysfunction of microbial communities in various human body sites has been shown to be associated with a variety of diseases raising the possibility of predicting diseases based on metagenomic samples. Although many studies have investigated this problem, there are no consensus on the optimal approaches for predicting disease status based on metagenomic samples. Using six human gut metagenomic datasets consisting of large numbers of colorectal cancer patients and healthy controls from different countries, we investigated different software packages for extracting relative abundances of known microbial genomes and for integrating mapping and assembly approaches to obtain the relative abundance profiles of both known and novel genomes. The random forests (RF) classification algorithm was then

SUBMITTER: Gao Y 

PROVIDER: S-EPMC8801753 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature