Evidence map›Paper›PMID 41044121›Full record

ArticleScientific reports2025

Discovering periodontitis biomarkers and therapeutic targets through bioinformatics and ensemble learning analysis.

Md Tanvir Hasan, Md Rakibul Islam, Nasser Raqe Alqhtani, Ali Robaian, Abdullah Saad Alqahtani, Fawaz Alqahtani, Abdulaziz Mohammed Alsakr, Huda Abutayyem, Sam Thomas Kuriadom, Maher A L Shayeb and 1 more

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In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Md Tanvir Hasan *Department of Software Engineering, Daffodil International University, Dhaka, Bangladesh.
Md Rakibul IslamDepartment of Software Engineering, Daffodil International University, Dhaka, Bangladesh.
Nasser Raqe AlqhtaniDepartment of Oral and Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, Prince Sattam Bin Abdullaziz University, Al-kharj, 11942, Saudi Arabia.
Ali RobaianConservative Dental Sciences Department, College of Dentistry, Prince Sattam Bin Abdulaziz University, Al-kharj, 11942, Saudi Arabia.
Abdullah Saad AlqahtaniDepartment of Preventive Dental Sciences, College of Dentistry, Prince Sattam Bin Abdulaziz University, Al-kharj, 11942, Saudi Arabia.
Fawaz AlqahtaniDepartment of Prosthetic Dental Sciences, College of Dentistry, Prince Sattam Bin Abdullaziz University, Al-kharj, 11942, Saudi Arabia.
Abdulaziz Mohammed AlsakrDepartment of Preventive Dental Sciences, College of Dentistry, Prince Sattam Bin Abdulaziz University, Al-kharj, 11942, Saudi Arabia.
Huda AbutayyemDepartment of Clinical Sciences, Center of Medical and Bio-Allied Health Sciences Research, College of Dentistry, Ajman University, Ajman, P.O. Box 346, United Arab Emirates.
Sam Thomas KuriadomDepartment of Clinical Sciences, Center of Medical and Bio-Allied Health Sciences Research, College of Dentistry, Ajman University, Ajman, P.O. Box 346, United Arab Emirates.
Maher A L ShayebDepartment of Clinical Sciences, Center of Medical and Bio-Allied Health Sciences Research, College of Dentistry, Ajman University, Ajman, P.O. Box 346, United Arab Emirates.
Mohammad Khursheed Alam *Preventive Dentistry Department, College of Dentistry, Jouf University, Sakaka, 72345, Saudi Arabia. mkalam@ju.edu.sa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Periodontitis, a prevalent inflammatory disease, leads to the progressive destruction of periodontal tissues and poses significant systemic health risks. Despite its widespread impact, the molecular mechanisms driving periodontitis remain poorly understood. This study integrates advanced ensemble machine learning models and bioinformatics approaches to elucidate the genetic basis of periodontitis. Using transcriptomic data from the gene expression omnibus (GEO) repository (GSE10334), we identified 21 common genes from bagging and boosting models, underscoring their critical role in disease pathophysiology. Protein-protein interaction (PPI) network analysis revealed hub genes (HNRNPC, TSR1, PLRG1, GOPC) with central roles in key biological pathways. Functional enrichment highlighted their involvement in actin filament regulation, immune response modulation, and RNA processing. Furthermore, mutation and copy number alteration (CNA) analyses revealed significant genetic diversity in these hub genes, particularly in diploid samples, with a high prevalence of missense and splice variants. Together, these findings advance our understanding of the molecular landscape of periodontitis, paving the way for novel biomarker discovery and targeted therapeutic strategies potentially leading to improved diagnostic and treatment approaches in periodontal care.

Indexed as

BiomarkersComputational BiologyMachine LearningPeriodontitisDNA Copy Number VariationsEnsemble LearningGene Expression ProfilingGene Regulatory NetworksHumansMutationProtein Interaction MapsTranscriptomeBiomarkersBiomarkerEnsemble learning modelMachine learningMutation and copy number alterationPeriodontitisProtein-protein interaction

Identifiers

PMID41044121
PMCPMC12494746

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.