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Comparison of Reverse‐Engineering Methods Using an in Silico Network
Author(s) -
CAMACHO DIOGO,
VERA LICONA PAOLA,
MENDES PEDRO,
LAUBENBACHER REINHARD
Publication year - 2007
Publication title -
annals of the new york academy of sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.712
H-Index - 248
eISSN - 1749-6632
pISSN - 0077-8923
DOI - 10.1196/annals.1407.006
Subject(s) - computer science , reverse engineering , benchmarking , variety (cybernetics) , data mining , bayesian network , dynamic bayesian network , machine learning , artificial intelligence , marketing , business , programming language
: The reverse engineering of biochemical networks is a central problem in systems biology. In recent years several methods have been developed for this purpose, using techniques from a variety of fields. A systematic comparison of the different methods is complicated by their widely varying data requirements, making benchmarking difficult. Also, because of the lack of detailed knowledge about most real networks, it is not easy to use experimental data for this purpose. This paper contains a comparison of four reverse‐engineering methods using data from a simulated network. The network is sufficiently realistic and complex to include many of the challenges that data from real networks pose. Our results indicate that the two methods based on genetic perturbations of the network outperform the other methods, including dynamic Bayesian networks and a partial correlation method.