The telecommunications industry is going through a technological revolution, involving a transition from multiple voice and data networks to integrated service networks using packet switching technologies.
However, most network-based methods so far have focused on a single type of high-throughput data, instead of integrating multiple data types with network information.
We have evaluated network performance for multiple data classes in a network with a single RSU deployed on a bidirectional road segment with transmission ranges of L = 900, 500, and 350 m, respectively.
The main feature of this methodology is that it can be used to develop dynamical models of gene regulatory networks integrating multiple data sources and combining any existing network inference methodology to identifying the network topology.
Because the one-dimensional layout of network nodes is created on the basis of the hierarchical modular organization of the network, a quick overview of one or multiple data tracks can often reveal network modules or sub-networks with interesting patterns, e.g. modules enriched with mutated or differentially expressed genes.
The issue of adaptive and distributed cross-layer resource allocation for energy efficiency in uplink code-division multiple-access (CDMA) wireless data networks is addressed.
The packet switching nature of data networks allows multiple connections to share the same transport medium.
While some tools have been developed to generate composite networks from multiple data sources (e.g., the Cytoscape [ 12] plugin CABIN [ 13], GraphWeb [ 14] and GeneMania [ 15]), they do not fully address the problems stated above.
Some newer pathway algorithms such as NetBox (Cerami et al., 2010) and Mutual Exclusivity Modules in Cancer (MEMo) (Ciriello et al., 2012) attempt to solve the problem of data integration in cancer to identify networks across multiple data types that are key to the oncogenic potential of samples.
Continuing previous years' trend [ 6, 7], several papers in this supplement demonstrated that integrative analysis that combines multiple data sources and utilizes interaction networks can often provide more stable and accurate results than analysis with a single data source or considering each gene as an independent entity.
