The network alignment problem in its abstract form can be formulated as an optimization problem with the goal of identifying an optimal mapping between the nodes of the input networks, which maximizes both sequence similarity of aligned proteins and conservation of their underlying interactions.
Given these graph representations of biological networks, the network alignment problem can be formulated as an optimization problem whose goal is to find the optimal mapping - either one-to-one or many-to-many - among a set of graphs that maximizes a scoring function that assesses the goodness of a given mapping.
This paper presents a modern heuristic method, Particle Swarm Optimization (PSO), to realize the optimal mapping by searching the best solution to the multiobjective optimization problem, where the objective functions are given with preferences.
This is essentially a combinatorial optimization problem with a exponentially large search space, which makes finding the optimal mapping practically infeasible for large networks.
**Assumes optimal mapping values for a lane run on a GA II (10 million mapped reads) Number of reads required in order to identify greater than 95% of 600 simulated binding sites distributed evenly over the 12 Mb S. cerevisiae genome with average fold enrichments of 2×, 5×, 10× and 50×.
In the algorithm of PSO, the problem of optimization can correspond to the process of food hunting, and the solution of optimal mapping correspond to the food's position.
The optimal mapping of a parallel program is strongly dependent on the granularity and network architecture.
Finally, we employ a traditional algorithm to find the optimal mapping.
However, the search for the optimal mapping of virtual networks is an NP-hard problem[6].
We hypothesize that intradermal TSC is the optimal mapping technique and can be used alone to identify SLNs.
We focus on the problem of finding the optimal mapping of pipeline algorithms on a ring of processors.
