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Related Experiment Video

Updated: Jun 16, 2026

A Web Tool for Generating High Quality Machine-readable Biological Pathways
08:01

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

BRPtools: An AutoML-Powered web platform for multiclass disease prediction from bulk blood RNA-seq data.

Yichen Guo1, Fengyuan Yang1, Xuelu Zhang1

  • 1Basic Medicine Research and Innovation Center for Novel Target and Therapeutic Intervention, Ministry of Education, College of Pharmacy, Chongqing Medical University, Chongqing 400016, China.

Molecular Therapy. Nucleic Acids
|June 15, 2026
PubMed
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This study integrates 134 blood RNA sequencing datasets for 88 diseases, achieving over 90% accuracy in disease prediction using AutoML. The BRPtools platform offers accessible transcriptome analysis for biomarker discovery and digital diagnostics.

Area of Science:

  • Genomics and Bioinformatics
  • Computational Biology
  • Translational Medicine

Background:

  • Blood transcriptomic profiling offers a minimally invasive diagnostic approach.
  • Integrating large, heterogeneous RNA sequencing (RNA-seq) datasets presents significant challenges.

Purpose of the Study:

  • To develop a robust and scalable method for multiclass prediction using integrated blood RNA-seq data.
  • To create an accessible platform for transcriptome-based disease diagnosis and biomarker discovery.

Main Methods:

  • Manual curation and uniform reprocessing of 134 human RNA-seq datasets (9,872 samples) covering 88 blood-related diseases.
  • Application of AutoGluon, an ensemble-based AutoML framework, for automated model selection and hyperparameter tuning.
  • Implementation of differential expression and pathway enrichment analyses for biological interpretation.
Keywords:
MT: bioinformaticsRNA-seqblood transcriptomicsmachine learningmulticlass disease predictionprecision medicine

Related Experiment Videos

Last Updated: Jun 16, 2026

A Web Tool for Generating High Quality Machine-readable Biological Pathways
08:01

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

Main Results:

  • High prediction accuracy (>90%) across most disease categories, demonstrating strong generalizability.
  • Identification of disease-associated transcriptomic signatures enriched in relevant Gene Ontology and KEGG pathways.
  • Development of BRPtools, a web-based platform with search and prediction modules for user-friendly analysis.

Conclusions:

  • The integrated approach and AutoML framework enable accurate and scalable transcriptome-based disease prediction.
  • BRPtools provides an accessible solution for diagnostic inference, validation, biomarker discovery, and precision medicine.
  • This work facilitates the translation of transcriptomic data into clinical applications.