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Benchmarking of Dynamic Bayesian Networks Inferred from Stochastic Time‐Series Data
Author(s) -
DAVID LAWRENCE A.,
WIGGINS CHRIS H.
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.1407.009
Subject(s) - dynamic bayesian network , computer science , inference , contrast (vision) , time series , data mining , series (stratigraphy) , interpolation (computer graphics) , missing data , artificial intelligence , machine learning , bayesian network , motion (physics) , paleontology , biology
: We seek to quantify the failure and success of dynamic Bayesian networks (DBNs), a popular tool for reverse‐engineering networks from time‐series data. In particular, we focus on data generated by continuous time processes (e.g., genetic expression) and sampled at discrete times. To facilitate analysis and interpretation, we employ a “minimal model” to generate arbitrary abundances of stochastic data from networks of known topologies, which are then sub‐sampled and in some cases interpolated. We find that DBNs perform relatively poorly when given data sets comparable to those used for genetic network inference. Interpolation does not appear to improve inference success. Finally, we contrast the performance of DBNs with results from linear regression on our synthetic data.