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
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.