MOTIVATION: The goal of neighborhood analysis is to find a set of genes (the neighborhood) that is similar to an initial 'seed' set of genes. Neighborhood analysis methods for network data are important in systems biology. If individual network connections are susceptible to noise, it can be advantageous to define neighborhoods on the basis of a robust interconnectedness measure, e.g. the topological overlap measure. Since the use of multiple nodes in the seed set may lead to more informative neighborhoods, it can be advantageous to define multi-node similarity measures. RESULTS: The pairwise topological overlap measure is generalized to multiple network nodes and subsequently used in a recursive neighborhood construction method. A local permutation scheme is used to determine the neighborhood size. Using four network applications and a simulated example, we provide empirical evidence that the resulting neighborhoods are biologically meaningful, e.g. we use neighborhood analysis to identify brain cancer related genes. AVAILABILITY: An executable Windows program and tutorial for multi-node topological overlap measure (MTOM) based analysis can be downloaded from the webpage (http://www.genetics.ucla.edu/labs/horvath/MTOM/).

Distributed Shared Memory (DSM) systems have been proposed to combine the programmability of traditional shared memory and the scalability of message-passing systems. Eager DSM systems can greatly reduce access latencies for remote data by keeping copies of shared values in local memory and updating them immediately whenever a shared datum changes. However, sharing all changes globally can limit the system performance. It is usually possible to transform a program into an equivalent form that generates much less traffic, and therefore executes much more efficiently. This paper describes a compile-time analysis model for transforming simple shared memory programs with parallelized loop structures into programs that are optimized for efficient execution on eager DSM systems.>