network, nodes represent genes and edges connect two genes if they exhibit a
positive or a negative genetic interaction.
Experimental strategies have been developed for detecting and mapping of
genetic interactions on a system-level scale. Synthetic Genetic Arrays (SGA),
introduced by Tonget al.( 2001 ), and Synthetic Lethal Analysis of Microarray
(SLAM), introduced by Ooiet al.( 2003 ), are commonly used experimental tech-
niques for large-scale genetic interaction data detection.
A GI network has a very small number of common links with a PPI network.
Therefore, GI networks represent valuable complementary information in integra-
tion procedures. The widely used database containing GI networks for various
organisms is BioGRID.^64
3.1.5 Signal-Transduction (ST) Networks
Signal transduction networks carry a signal from the cell’s surface into the nucleus.
Nodes in this network represent proteins, and directed edges between the proteins
represent a signal propagating from one protein to another.^65 Cell signaling is very
important. Its perturbation can lead to emergence of many complex diseases, such
as cancer and diabetes. In addition to understanding diseases, these networks are
suitable for modeling an organism’s response to various stimuli.
However, low availability of these data has limited the number of computational
studies on these networks.^66 In some of the studies, an ST network is constructed
from the cell’s signaling pathways retrieved from KEGG database.^67 The only
available database containing ST networks information is the TRANSPATH signal
transduction database.^68
3.1.6 Gene-Disease Association (GDA) Bipartite Network
Gene-disease association data are the main component of the integration method-
ologies of many disease-related studies. These relational data can be modeled using
a bipartite network representation, where nodes representing human diseases in one
partition are connected to nodes in the other partition that contains nodes
corresponding to disease-related genes. The three commonly used data repositories
for retrieving gene-disease associations are the following ones:
(^64) Chatr-Aryamontri et al. ( 2013 ).
(^65) Eungdamrong and Iyengar ( 2004 ).
(^66) Eungdamrong and Iyengar ( 2004 ) and Soyer et al. ( 2006 ).
(^67) Zˇitnik et al. ( 2013 ).
(^68) Schacherer et al. ( 2001 ).
150 V. Gligorijevic ́and N. Pržulj