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Abstract

Endodontic infections represent a complex polymicrobial ecology that poses significant challenges in dental and oral health treatment. This study explores the bacterial communities present in endodontic infections through comprehensive omics datasets. The study implements feature selection techniques to effectively reduce the dimensionality of high-throughput sequencing data while maintaining biological relevance. Using 16S rRNA gene sequencing, metagenomics and database approaches, we selected diverse bacterial communities in primary and persistent endodontic infections. The implementation of our feature selection algorithm demonstrated a 78% reduction in dimensionality while preserving 93% of the classification accuracy. Principal component analysis and hierarchical clustering revealed distinct bacterial community structures between different types of endodontic infections. Functional analysis indicated prevalent metabolic pathways associated with virulence factors and antibiotic resistance. This research provides a robust methodological framework for analyzing complex microbiome data in endodontic infections, contributing to improved diagnostic and therapeutic strategies based on bacterial community profiles