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ORIGINAL RESEARCH article

Front. Oral. Health
Sec. Preventive Dentistry
Volume 5 - 2024 | doi: 10.3389/froh.2024.1462845
This article is part of the Research Topic Revolutionizing Oral Healthcare: The Pivotal Role of Artificial Intelligence in Diagnosing and Treating Oral Diseases View all 3 articles

Gradient Boosting-Based Classification of Interactome Hub Genes in Periimplantitis With Periodontitis -An Integrated Bioinformatic Approach

Provisionally accepted
Pradeep Kumar Yadalam Pradeep Kumar Yadalam 1Sarvagya Sharma Sarvagya Sharma 1Prabhu Natarajan Prabhu Natarajan 2*Carlos M. Ardila Carlos M. Ardila 3*
  • 1 Saveetha Dental College And Hospitals, Chennai, Tamil Nadu, India
  • 2 Ajman University, Ajman, Ajman, United Arab Emirates
  • 3 University of Antioquia, Medellín, Colombia

The final, formatted version of the article will be published soon.

    Peri-implantitis, a destructive inflammatory condition affecting the tissues surrounding dental implants, shares pathological similarities with periodontitis, a chronic inflammatory disease that impacts the supporting structures of natural teeth.This study utilizes a network-based approach to classify interactome hub genes associated with peri-implantitis and periodontitis, aiming to improve understanding of disease mechanisms and identify potential therapeutic targets.We employed gradient boosting and Weighted Gene Co-expression Network Analysis (WGCNA) to predict and classify these interactome hub genes. Gene expression data related to these diseases were sourced from the NCBI GEO dataset GSE223924, and differential gene expression analysis was conducted using the NCBI GEO R tool.Through WGCNA, we constructed a co-expression network to identify key hub genes, while gradient boosting was used to predict these hub genes.Our analysis revealed a co-expression network comprising 216 genes, including prominent hub genes such as IL17RC, CCN2, BMP7, TPM1, and TIMP1, which are implicated in periodontal disease. The gradient boosting model achieved an 88.2% accuracy in classifying interactome hub genes in samples related to peri-implantitis and periodontitis. These identified genes play roles in inflammation, osteoclast genesis, angiogenesis, and immune response regulation. This study highlights essential hub genes and molecular pathways associated with peri-implantitis and periodontitis, suggesting potential therapeutic targets for developing innovative treatment strategies.

    Keywords: Peri-Implantitis, machine learning, Computational Biology, Genes, Gene Regulatory Networks

    Received: 10 Jul 2024; Accepted: 12 Nov 2024.

    Copyright: © 2024 Yadalam, Sharma, Natarajan and Ardila. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Prabhu Natarajan, Ajman University, Ajman, Ajman, United Arab Emirates
    Carlos M. Ardila, University of Antioquia, Medellín, Colombia

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