PT  - JOURNAL ARTICLE
AU  - Stefano Beretta
AU  - Mauro Castelli
AU  - Ivo Gonçalves
AU  - Ivan Merelli
AU  - Daniele Ramazzotti
TI  - Combining Bayesian Approaches and Evolutionary Techniques for the Inference of Breast Cancer Networks
AID  - 10.1101/115261
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 115261
4099  - http://biorxiv.org/content/early/2017/03/09/115261.short
4100  - http://biorxiv.org/content/early/2017/03/09/115261.full
AB  - 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.