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ORIGINAL RESEARCH article

Front. Pharmacol.
Sec. Experimental Pharmacology and Drug Discovery
Volume 16 - 2025 | doi: 10.3389/fphar.2025.1545392
This article is part of the Research Topic Advances in Biomarkers and Drug Targets: Harnessing Traditional and AI Approaches for Novel Therapeutic Mechanisms View all articles

Integrating Traditional Omics and Machine Learning Approaches to Identify Microbial Biomarkers and Therapeutic Targets in Pediatric Inflammatory Bowel Disease

Provisionally accepted
Lanlan Li Lanlan Li 1Xuzai Deng Xuzai Deng 1Shuge Wang Shuge Wang 1Zhuyuan Huang Zhuyuan Huang 2Huang Tao Huang Tao 3,4*
  • 1 Tianyou Hospital, Wuhan University of Science and Technology, Wuhan, Hubei Province, China
  • 2 WUHAN NO.15 SENIOR MIDDLE SCHOOL, WUHAN, China
  • 3 Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
  • 4 Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hebei Province, China

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

    Background: Pediatric inflammatory bowel disease (IBD), especially Crohn's disease, significantly affects gut health and quality of life. Although gut microbiome research has advanced, identifying reliable biomarkers remains difficult due to microbial complexity.We used RNA-seq-based microbial profiling and machine learning (ML) to find robust biomarkers in pediatric IBD. Microbial taxa were profiled at phylum, genus, and species levels using kraken2 on Crohn's disease and non-IBD ileal biopsies. We performed abundance-based analyses and applied four ML models (Logistic Regression, Random Forest, Support Vector Machine, XGBoost) to detect discriminative taxa. An independent cohort of 36 pediatric stool samples assessed by 16S rRNA sequencing validated top ML results.Results: Traditional abundance-based methods showed compositional shifts but identified few consistently significant taxa. ML models had better discriminatory performance, with XGBoost outperforming others and pinpointing Orthotospovirus and Vescimonas as key genera. These findings were confirmed in the validation cohort, where only one traditionally noted genus, Actinomyces, maintained significance.Discussion: Integrating conventional omics with AI-driven analytics boosts reproducibility and clinical relevance of microbial biomarker discovery, opening new possibilities for targeted therapies and precision medicine in pediatric IBD.

    Keywords: pediatric IBD, biomarkers, RNA-Seq, machine learning, therapeutic targets, reproducibility

    Received: 14 Dec 2024; Accepted: 14 Jan 2025.

    Copyright: © 2025 Li, Deng, Wang, Huang and Tao. 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: Huang Tao, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.