Identification of Biomarkers and Trajectories of Prostate Cancer Progression: A Bioinformatics Fusion of Weighted Correlation Network Analysis and Machine Learning
Raheleh Sheibani‐Tezerji, Carlos Uziel Pérez Malla, Gabriel Wasinger, Katarina Mišura, Astrid Haase, Anna Malzer, Jessica Kalla, Loan Tran, Gerda Egger
This study identifies biomarkers and trajectories of prostate cancer progression using bioinformatics fusion of weighted correlation network analysis and machine learning, finding the EZH2-TROAP axis as a key trajectory and 58 validated biomarkers.
bioRxiv · Mar 2, 2023 · PreprintBioinformatics