Periodontitis is an inflammatory disease involving complex interactions between oral microorganisms and the host immune response. Understanding the structure of the microbiota community associated with periodontitis is essential for improving classifications and diagnoses of various types of periodontal diseases and will facilitate clinical decision-making. In this study, we used a 16S rRNA metagenomics approach to investigate and compare the compositions of the microbiota communities from 76 subgingival plagues samples, including 26 from healthy individuals and 50 from patients with periodontitis. Furthermore, we propose a novel feature selection algorithm for selecting features with more information from many variables with a combination of these features and machine learning methods were used to construct prediction models for predicting the health status of patients with periodontal disease. We identified a total of 12 phyla, 124 genera, and 355 species and observed differences between health- and periodontitis-associated bacterial communities at all phylogenetic levels. We discovered that the genera Porphyromonas, Treponema, Tannerella, Filifactor, and Aggregatibacter were more abundant in patients with periodontal disease, whereas Streptococcus, Haemophilus, Capnocytophaga, Gemella, Campylobacter, and Granulicatella were found at higher levels in healthy controls. Using our feature selection algorithm, random forests performed better in terms of predictive power than other methods and consumed the least amount of computational time.

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We identified 35 candidates and validated 6 genes that may be associated with areca nut-induced oral cancer. Loss of Ches1 may be attributed to areca nut extract-induced oral carcinogenesis.

Arabidopsis thaliana CBM45 and CBM53 were used to validate the FIA-based prediction model. The predicted ligand-binding residues residing on the surface in the hypothetical structures were verified to be ligand-binding residues. In the absence of 3D structural information, FIA demonstrated significant improvement in the estimation of sequence similarity and identity for a total of 808 sequences from 11 different CBM families as compared with six leading tools by Friedman rank test.