Composition Analysis and Feature Selection of the Oral Microbiota Associated with Periodontal Disease
Joint Authors
Chen, Wen-Pei
Chang, Shih-Hao
Liou, Ming-Li
Tsai, Suh-Jen Jane
Lin, Yaw-Ling
Tang, Chuan Yi
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-11-15
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Chen, Wen-Pei& Chang, Shih-Hao& Tang, Chuan Yi& Liou, Ming-Li& Tsai, Suh-Jen Jane& Lin, Yaw-Ling. 2018. Composition Analysis and Feature Selection of the Oral Microbiota Associated with Periodontal Disease. BioMed Research International،Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1125656
Modern Language Association (MLA)
Chen, Wen-Pei…[et al.]. Composition Analysis and Feature Selection of the Oral Microbiota Associated with Periodontal Disease. BioMed Research International No. 2018 (2018), pp.1-14.
https://search.emarefa.net/detail/BIM-1125656
American Medical Association (AMA)
Chen, Wen-Pei& Chang, Shih-Hao& Tang, Chuan Yi& Liou, Ming-Li& Tsai, Suh-Jen Jane& Lin, Yaw-Ling. Composition Analysis and Feature Selection of the Oral Microbiota Associated with Periodontal Disease. BioMed Research International. 2018. Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1125656
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1125656