Target Discovery and Validation Reviews and Protocols

Gene Networks 53
Bayesian network with moving boxcel median filtering to capture such interactions.
8. Imoto et al. (29) provided a general framework to combine microarray data and
various types of biological data aimed at estimating gene networks. Tamada et al. (30)
used promoter elements detection method together with the Bayesian networks
for simultaneous estimation of gene networks and transcription factor binding
sites. Nariai et al. (31,32) used protein–protein interaction data for refining gene
networks estimated from microarray data. Tamada et al. (33) used evolutionary
information to estimate gene networks of two distinct organisms.
9. The dynamic Bayesian networks (34–36) can estimate gene networks with cyclic
relations from time-course microarray data. Also, Yoshida et al. (37) proposed a
dynamic linear model with Markov switching to estimate time-dependent gene
networks that can allow structural change of the estimated gene network depending
on time.
10. For measuring reliability of an estimated edge, the bootstrap (38) is a useful
method. As an advanced method of the bootstrap method, Kamimura et al. (39)
used the multiscale bootstrap method (40) for computing more acculate bootstrap
probability for the estimated edge in the Bayesian network.
11. Belief propagation (41) is an algorithm to infer the posterior marginal probability
of a variable in the Bayesian networks. The exact computation of this algorithm is
limited for singly connected networks; however, it takes only linear time in practice.
Generally, calculating the probability in an arbitrary Bayesian network is
known to be NP-hard (42). Therefore, several approximation algorithms have been
proposed such as the loopy belief propagation (41), the junction tree algorithm
(43), and sampling based methods such as Markov chain Monte Carlo (44).
Acknowledgments
We thank our colleagues and collaborators Hideo Bannai, Michiel de Hoon,
Ryo Yoshida, Takao Goto, Sunyong Kim, Naoki Nariai, Sascha Ott, Tomoyuki
Higuchi, Hidetoshi Shimodaira, Sachiyo Aburatani, Kousuke Tashiro, and
Satoru Kuhara.
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