RNAcare: integrating clinical data with transcriptomic evidence using rheumatoid arthritis as a case study

Area of Science:

• Computational biology
• Genomics
• Clinical informatics

Background:

• Gene expression analysis is vital for understanding disease but challenging for clinicians due to data complexity.
• Current tools often lack integration of clinical data and patient-specific analyses.
• Advanced computational tools are needed to link gene expression to clinical symptoms.

Purpose of the Study:

• To present RNAcare, an interactive platform for analyzing transcriptomic and clinical data.
• To enable researchers to explore gene expression patterns in relation to clinical features.
• To facilitate hypothesis generation and visualization for clinical research.

Main Methods:

• Developed the RNAcare platform for integrating transcriptomic and clinical data.
• Implemented interactive and reproducible analysis workflows.
• Utilized machine learning approaches to correlate gene expression with clinical symptoms.

Main Results:

• RNAcare successfully integrates gene expression and clinical data for patient subgroup analysis.
• The platform facilitated the identification of inflammation-related genes linked to pain and fatigue in rheumatoid arthritis.
• Confirmed previous findings and generated illustrative visualizations for research support.

Conclusions:

• RNAcare is a novel computational platform for real-time interpretation of clinical and transcriptomics data.
• The platform supports user-generated and published datasets.
• RNAcare is accessible online with its source code available.