Combining Bayesian approaches and evolutionary techniques for the inference of breast cancer networks

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)
3 Downloads (Pure)

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.

Original languageEnglish
Title of host publicationECTA 2016 - 8th International Conference on Evolutionary Computation Theory and Applications
EditorsJuan Julian Merelo, Jose M. Cadenas, Fernando Melicio, Antonio Dourado, Antonio Ruano, Joaquim Filipe, Kurosh Madani
PublisherSciTePress
Pages217-224
Number of pages8
ISBN (Electronic)9789897582011
DOIs
Publication statusPublished - 1 Jan 2016
Event8th International Joint Conference on Computational Intelligence, IJCCI 2016 - Porto, Portugal
Duration: 9 Nov 201611 Nov 2016

Publication series

NameIJCCI 2016 - Proceedings of the 8th International Joint Conference on Computational Intelligence
Volume1

Conference

Conference8th International Joint Conference on Computational Intelligence, IJCCI 2016
CountryPortugal
CityPorto
Period9/11/1611/11/16

Keywords

  • Bayesian graphical models
  • Breast cancer
  • Genetic algorithms
  • Network inference

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