样本编号	SAMC4687013
外部数据库编号	GSA-Human: HRS1557760
样品名称	P14
样本标题	Patient of IBD feces
样品类型	Environmental/Metagenome Genomic Sequences MIMS.me -- human-gut
物种名称	Colon
描述信息	This sample consists of fecal specimens from pediatric patients diagnosed with inflammatory bowel disease (IBD). All participants are under the age of 18 and meet the established diagnostic criteria for IBD.
样本属性	*该样本包含更多受控访问信息，请通过GSA-Human系统申请项目HRA010328数据获取。
发布日期	2025-02-08
项目编号	PRJCA035418
提交者	Tao Huang (24867509@qq.com)
提交单位	Maternal and Child Health Hospital of Hubei Province
提交日期	2025-02-09

样本包含数据信息

资源名称	描述
GSA-Human (1)	-
HRA010328 (Controlled Access)	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.

资源名称

描述

GSA-Human (1)

HRA010328 (Controlled Access)

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.