Combining Bayesian Approaches and Evolutionary Techniques for the Inference of Breast Cancer Networks
This work addresses the challenge of modeling causal relationships in breast cancer networks for molecular biology researchers, but it is incremental as it focuses on comparing existing methods rather than introducing new ones.
The authors tackled the problem of inferring gene/protein networks from breast cancer data, which is complicated by large-scale unknowns and small sample sizes, by conducting a comparative study of state-of-the-art heuristics using Bayesian Graphical Models to analyze their performance in network structure inference.
Gene and protein networks are very important to model complex large-scale systems in molecular biology. Inferring or reverseengineering such networks can be defined as the process of identifying gene/protein interactions from experimental data through computational analysis. However, this task is typically complicated by the enormously large scale of the unknowns in a rather small sample size. Furthermore, when the goal is to study causal relationships within the network, tools capable of overcoming the limitations of correlation networks are required. In this work, we make use of Bayesian Graphical Models to attach this problem and, specifically, we perform a comparative study of different state-of-the-art heuristics, analyzing their performance in inferring the structure of the Bayesian Network from breast cancer data.