A Graph Database Approach for Biomarker Discovery

2111 

Contributed Abstract 

Poster 

Yang Liu (1) (,2), Jason Huse (3), Kasthuri Kannan (3)

(1) SPH in The University of Texas Health Science Center at Houston , TX, (2) MD Anderson Cancer Center, N/A, (3) MD Anderson Cancer Center, Houston, TX

Jason Huse  
MD Anderson Cancer Center

Kasthuri Kannan  
MD Anderson Cancer Center

Yang Liu  
SPH in The University of Texas Health Science Center at Houston | MD Anderson Cancer Center

Yang Liu  
SPH in The University of Texas Health Science Center at Houston | MD Anderson Cancer Center

Traditional statistical and machine learning methods often struggle to capture complex, interconnected relationships within biological data that enable biomarker discovery. We present a novel graph-based framework that leverages graph neural networks and network-based feature engineering to identify predictive biomarkers. Our approach constructs several biological networks by integrating gene expression data and clinical attributes using a graph database, providing multiple representations of patient-specific relationships. We employ graph learning techniques to ensemble graphs to identify candidates using hierarchical, feature-based and filter-based methods. Using three independent datasets, we demonstrate that our method improves predictive performance compared to conventional machine learning models. This scalable and interpretable strategy has broad applications in biomarker discovery across diverse disease domains.

graph database|graph neural network|biomarker|feature engineering|feature selection|

Biopharmaceutical Section

Biomarkers and Endpoint Validation