PriPath: identifying dysregulated pathways from differential gene expression via grouping, scoring, and modeling with...

Area of Science:

• Genomics and Bioinformatics
• Systems Biology
• Computational Biology

Background:

• Cellular homeostasis depends on gene interactions within networks, and disruptions can lead to disease.
• The Kyoto Encyclopedia of Genes and Genomes (KEGG) organizes gene functions into pathways, crucial for understanding biological processes.
• Current gene expression analysis methods often overlook the rich information within KEGG pathways, necessitating improved approaches.

Purpose of the Study:

• To develop a novel method, PriPath, for analyzing gene expression data in conjunction with KEGG pathways.
• To improve the detection of dysregulated pathways associated with diseases by integrating prior biological knowledge.
• To establish a foundation for precision medicine through enhanced pathway analysis.

Main Methods:

• PriPath utilizes KEGG pathways as a grouping function within a machine learning framework.
• A machine learning model is trained to distinguish between disease and control samples using pathway-based gene groupings.
• The approach involves grouping, scoring, and modeling gene expression data against KEGG pathways.

Main Results:

• PriPath was tested on 13 diverse gene expression datasets, including various cancers.
• The method successfully identified biologically and clinically relevant KEGG pathways associated with differentially expressed genes.
• Manual validation confirmed that PriPath's top predictions are supported by existing scientific literature.

Conclusions:

• PriPath effectively identifies dysregulated pathways, offering potential for medical diagnostics.
• The approach can be advanced for patient stratification and identifying druggable targets, contributing to precision medicine.
• PriPath enhances the interpretation of gene expression data by leveraging KEGG pathway knowledge.