Machine learning reveals microbiome differences by periodontitis severity.
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| Title: | Machine learning reveals microbiome differences by periodontitis severity. |
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| Authors: | Seo SH; Department of Laboratory Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.; Department of Laboratory Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.; Department of Genomic Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea., Lee JW; Accugene Inc., Incheon, Republic of Korea., Oh S; Department of Laboratory Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea., Hong JS; Department of Periodontology, Section of Dentistry, Seoul National University Bundang Hospital, Seongnam, Republic of Korea., Lee BS; Accugene Inc., Incheon, Republic of Korea., Kwon SJ; Accugene Inc., Incheon, Republic of Korea., Kim KS; Department of Periodontology, Section of Dentistry, Seoul National University Bundang Hospital, Seongnam, Republic of Korea., Park JS; Department of Periodontology, Korea University Anam Hospital, Seoul, Republic of Korea., Heo JS; Department of Pediatrics, Seoul National University Children's Hospital, Seoul, Republic of Korea.; Department of Pediatrics, Seoul National University College of Medicine, Seoul, Republic of Korea., Ahn KH; Department of Obstetrics and Gynecology, Korea University College of Medicine, Seoul, Republic of Korea., Lee HJ; Department of Genomic Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.; Department of Periodontology, Section of Dentistry, Seoul National University Bundang Hospital, Seongnam, Republic of Korea., Park KU; Department of Laboratory Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.; Department of Laboratory Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea. |
| Source: | PloS one [PLoS One] 2026 May 21; Vol. 21 (5), pp. e0349686. Date of Electronic Publication: 2026 May 21 (Print Publication: 2026). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1932-6203 |
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| DOI: | 10.1371/journal.pone.0349686 |
