Composition Analysis and Feature Selection of the Oral Microbiota Associated with Periodontal Disease.
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 periodo...
| Publicado en: | BioMed Research International pp. 1 - 15 |
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| Autores principales: | , , , , , |
| Formato: | algorithm research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
11/15/2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=133026440&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 133026440 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 11/15/2018 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 133026440 133026440 133026440 10.1155/2018/3130607 133026440 ppf: 1 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Composition Analysis and Feature Selection of the Oral Microbiota Associated with Periodontal Disease. aug: au: Chen, Wen-Pei Chang, Shih-Hao Tang, Chuan-Yi Liou, Ming-Li Tsai, Suh-Jen Jane Lin, Yaw-Ling affil: Department of Applied Chemistry, Providence University, Taichung City, Taiwan sug: subj: Periodontal Diseases Microbiology Genome Human Periodontitis Health Status Machine Learning Gram-Negative Anaerobic Bacteria Tannerella Streptococcus Haemophilus Gram-Negative Aerobic Bacteria Gram-Positive Bacteria Campylobacter Phylogenetics ab: 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. pubtype: Academic Journal doctype: algorithm research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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