Recent advances in clustering methods for protein interaction networks

The increasing availability of large-scale protein-protein interaction data has made it possible to understand the basic components and organization of cell machinery from the network level. The arising challenge is how to analyze such complex interacting data to reveal the principles of cellular organization, processes and functions. Many studies have shown that clustering protein interaction network is an effective approach for identifying protein complexes or functional modules, which has become a major research topic in systems biology. In this review, recent advances in clustering methods for protein interaction networks will be presented in detail. The predictions of protein functions and interactions based on modules will be covered. Finally, the performance of different clustering methods will be compared and the directions for future research will be discussed

Elixir: synthesis of parallel irregular algorithms

Algorithms in new application areas like machine learning and data analytics usually operate on unstructured sparse graphs. Writing efficient parallel code to implement these algorithms is very challenging for a number of reasons. First, there may be many algorithms to solve a problem and each algorithm may have many implementations. Second, synchronization, which is necessary for correct parallel execution, introduces potential problems such as data-races and deadlocks. These issues interact in subtle ways, making the best solution dependent both on the parallel platform and on properties of the input graph. Consequently, implementing and selecting the best parallel solution can be a daunting task for non-experts, since we have few performance models for predicting the performance of parallel sparse graph programs on parallel hardware. This dissertation presents a synthesis methodology and a system, Elixir, that addresses these problems by (i) allowing programmers to specify solutions at a high level of abstraction, and (ii) generating many parallel implementations automatically and using search to find the best one. An Elixir specification consists of a set of operators capturing the main algorithm logic and a schedule specifying how to efficiently apply the operators. Elixir employs sophisticated automated reasoning to merge these two components, and uses techniques based on automated planning to insert synchronization and synthesize efficient parallel code. Experimental evaluation of our approach demonstrates that the performance of the Elixir generated code is competitive to, and can even outperform, hand-optimized code written by expert programmers for many interesting graph benchmarks.Computer Science

A Parallel Algorithm for Clustering Protein-Protein Interaction Networks

The increasing availability of interaction graphs requires new resource-efficient tools capable of extracting valuable biological knowledge from these networks. In this paper we report on a novel parallel implementation of Girvan and Newman’s clustering algorithm that is capable of running on clusters of computers. Our parallel implementation achieves almost linear speed-up up to 32 processors and allows us to run this computationally intensive algorithm on large protein-protein interaction networks. Preliminary experiments show that the algorithm has very high accuracy in identifying functional related protein modules. Software will be made available in the public domain a