Target Discovery and Validation Reviews and Protocols

48 Imoto et al.
Fig. 8. Strategy to identify the drug active pathways. The red nodes represent drugactive
genes considered to be independent on their parent genes. The green nodes are
parent-active genes considered to be affected by their parent genes. The red arrows represent
the active paths (13).
To avoid these problems, we use Bayesian networks for discrete data for the
probabilistic inference after estimating the structure of the gene network. We
convert the estimated Bayesian network and nonparametric regression model
into the Bayesian network for discrete data (Fig. 2b). The drug-active pathways
are identified for every time-point (Fig. 2c). This step is based on the
identification of affected genes directly and indirectly by the drug. However, it
is possible that several genes might still be falsely determined as the affected
genes. Therefore, we define time-expanded network (Fig. 8) and consider the
time-dependent relationships among the estimated affected genes to reduce the
number of false positives. The definition of the time-dependent network is given
below (see Subheading 3.2.2.). The connected components in the timeexpanded
network are selected as the estimated drug-active pathways (Fig. 2d).
This step also can reduce the number of falsely identified pathways.
The identification of drug-active pathways is done based on the probabilistic
inference on the Bayesian networks for discrete data. For this purpose, we convert
the Bayesian network and nonparametric regression model that was used for structural
learning of the gene network into the Bayesian network for discrete data.
At first, we need to discretize the expression values into three categories. We
regard expression values that are greater than a certain threshold h (or less than
h –1 ) as “overexpreesed” (or “suppressed”) and otherwise as “unchanged.” The
threshold h is determined so that the network estimated by the Bayesian network
for discrete data with h becomes the closest to the network from the Bayesian