A network-based approach to identify expression modules underlying rejection in pediatric liver transplantation.

ABSTRACT: Selecting the right immunosuppressant to ensure rejection-free outcomes poses unique challenges in pediatric liver transplant (LT) recipients. A molecular predictor can comprehensively address these challenges. Currently, there are no well-validated blood-based biomarkers for pediatric LT recipients before or after LT. Here, we discover and validate separate pre- and post-LT transcriptomic signatures of rejection. Using an integrative machine learning approach, we combine transcriptomics data with the reference high-quality human protein interactome to identify network module signatures, which underlie rejection. Unlike gene signatures, our approach is inherently multivariate and more robust to replication and captures the structure of the underlying network, encapsulating additive effects

SUBMITTER: Ningappa M 

PROVIDER: S-EPMC9044102 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature