Combining Bayesian Approaches and Evolutionary Techniques for the Inference of Breast Cancer Networks

Stefano Beretta, Mauro Castelli, Ivo Gonçalves, Ivan Merelli, Daniele Ramazzotti

2016

Abstract

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.

Paper Citation

in Harvard Style

Beretta S., Castelli M., Gonçalves I., Merelli I. and Ramazzotti D. (2016). Combining Bayesian Approaches and Evolutionary Techniques for the Inference of Breast Cancer Networks . In Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2016) ISBN 978-989-758-201-1, pages 217-224. DOI: 10.5220/0006064102170224

in Bibtex Style

@conference{ecta16,
author={Stefano Beretta and Mauro Castelli and Ivo Gonçalves and Ivan Merelli and Daniele Ramazzotti},
title={Combining Bayesian Approaches and Evolutionary Techniques for the Inference of Breast Cancer Networks},
booktitle={Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2016)},
year={2016},
pages={217-224},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006064102170224},
isbn={978-989-758-201-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2016)
TI - Combining Bayesian Approaches and Evolutionary Techniques for the Inference of Breast Cancer Networks
SN - 978-989-758-201-1
AU - Beretta S.
AU - Castelli M.
AU - Gonçalves I.
AU - Merelli I.
AU - Ramazzotti D.
PY - 2016
SP - 217
EP - 224
DO - 10.5220/0006064102170224