Predicting foodborne pathogens and probiotics taxa within poultry-related microbiomes using a machine learning approach.

ABSTRACT:

Background

Microbiomes that can serve as an indicator of gut, intestinal, and general health of humans and animals are largely influenced by food consumed and contaminant bioagents. Microbiome studies usually focus on estimating the alpha (within sample) and beta (similarity/dissimilarity among samples) diversities. This study took a combinatorial approach and applied machine learning to microbiome data to predict the presence of disease-causing pathogens and their association with known/potential probiotic taxa. Probiotics are beneficial living microorganisms capable of improving the host organism's digestive system, immune function and ultimately overall health. Here, 16 S rRNA gene high-throughput Illumina sequencing of temporal pre-harvest (feces, soil) samples of 42 pastured p

SUBMITTER: Ayoola MB 

PROVIDER: S-EPMC10648331 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature