NGDC - GSA for Human

HRA010328

HRA010328.xlsx

Title:

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

Release date:

2025-02-08

Description:

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.

Data Accessibility:

Controlled access Request Data

BioProject:

PRJCA035418

Study type:

Human metagenome

Data Access Committee

For each controlled access study, there is a corresponding Data Access Committee(DAC) to determine the access permissions. Access to actual data files is not managed by NGDC.

DAC NO.:

HDAC005374

DAC name:

PIDB

Contact person:

Huang Tao

Email:

24867509@qq.com

Description:

it's a IBD group

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