TY - GEN
T1 - Modeling genetic networks
T2 - 5th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2007
AU - Rubio-Escudero, Cristina
AU - Harari, Oscar
AU - Cordón, Oscar
AU - Zwir, Igor
PY - 2007
Y1 - 2007
N2 - Biomedical research has been revolutionized by high-throughput techniques and the enormous amount of biological data they are able to generate. The interest shown over network models and systems biology is rapidly raising. Genetic networks arise as an essential task to mine these data since they explain the function of genes in terms of how they influence other genes. Many modeling approaches have been proposed for building genetic networks up. However, it is not clear what the advantages and disadvantages of each model are. There are several ways to discriminate network building models, being one of the most important whether the data being mined presents a static or dynamic fashion. In this work we compare static and dynamic models over a problem related to the inflammation and the host response to injury. We show how both models provide complementary information and cross-validate the obtained results.
AB - Biomedical research has been revolutionized by high-throughput techniques and the enormous amount of biological data they are able to generate. The interest shown over network models and systems biology is rapidly raising. Genetic networks arise as an essential task to mine these data since they explain the function of genes in terms of how they influence other genes. Many modeling approaches have been proposed for building genetic networks up. However, it is not clear what the advantages and disadvantages of each model are. There are several ways to discriminate network building models, being one of the most important whether the data being mined presents a static or dynamic fashion. In this work we compare static and dynamic models over a problem related to the inflammation and the host response to injury. We show how both models provide complementary information and cross-validate the obtained results.
UR - http://www.scopus.com/inward/record.url?scp=38049034451&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-71783-6_8
DO - 10.1007/978-3-540-71783-6_8
M3 - Conference contribution
AN - SCOPUS:38049034451
SN - 9783540717829
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 78
EP - 89
BT - Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics - 5th European Conference, EvoBIO 2007, Proceedings
PB - Springer Verlag
Y2 - 11 April 2007 through 13 April 2007
ER -