GBDR: a Bayesian model for precise prediction of pathogenic microorganisms using 16S rRNA gene sequences.

ABSTRACT:

Background

Recent evidences have suggested that human microorganisms participate in important biological activities in the human body. The dysfunction of host-microbiota interactions could lead to complex human disorders. The knowledge on host-microbiota interactions can provide valuable insights into understanding the pathological mechanism of diseases. However, it is time-consuming and costly to identify the disorder-specific microbes from the biological "haystack" merely by routine wet-lab experiments. With the developments in next-generation sequencing and omics-based trials, it is imperative to develop computational prediction models for predicting microbe-disease associations on a large scale.

Results

Based on the known microbe-disease associations derived from the Hum

SUBMITTER: Huang YA 

PROVIDER: S-EPMC8925046 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature