Bayesian Learning Artificial Neural Networks for Modeling Survival Outcomes using Microarray Gene Expression Data
Published in Providence, RI, 2022
Project Summary:
In this extension project, we developed an instructive, introductory extension post using the R statistical programming environment, Python, HTML, and gene expression profile data from patients with primary bladder cancer downloaded from the Gene Expression Omnibus (GEO) to show how Bayesian inference can be applied in modeling survival outcomes in patients with primary bladder cancer using single-layer artificial neural networks with Bayesian Learning. You can read the full post here.
Keywords: Bayesian artificial neural networks, Survival modeling, Primary bladder cancer, Gene expression profiles, Micro-array data, Gene expression Omnibus (GEO), R, Python, Extension post.