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Authors: Stefano Beretta 1 ; Mauro Castelli 2 ; Ivo Gonçalves 2 ; Ivan Merelli 3 and Daniele Ramazzotti 4

Affiliations: 1 Universitá degli Studi di Milano Bicocca, Italy ; 2 Universidade Nova de Lisboa, Portugal ; 3 Ist. di Tecnologie Biomediche, Italy ; 4 Stanford University, United States

ISBN: 978-989-758-201-1

Keyword(s): Bayesian Graphical Models,Breast Cancer,Genetic Algorithms,Network Inference.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biocomputing and Complex Adaptive Systems ; Computational Intelligence ; Evolutionary Computing ; Genetic Algorithms ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Symbolic Systems

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.

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Paper citation in several formats:
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 3: ECTA, (IJCCI 2016) ISBN 978-989-758-201-1, pages 217-224. DOI: 10.5220/0006064102170224

@conference{ecta16,
author={Stefano Beretta. and Mauro Castelli. and Ivo Gon\c{C}alves. 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 3: ECTA, (IJCCI 2016)},
year={2016},
pages={217-224},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006064102170224},
isbn={978-989-758-201-1},
}

TY - CONF

JO - Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 3: 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

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