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The Flag-based Algorithm-A Novel Greedy Method that Optimizes Protein Communities Detection

National University of Ireland, Cork; Department of Computer Science; National University of Ireland, Cork; Department of Computer Science
Int. J. of Computers 04/2011; VI:33-44.

ABSTRACT Proteins and the networks they determine, called interactome net-works, have received attention at an important degree during the last years, because they have been discovered to have an influence on some complex bi-ological phenomena, such as problematic disorders like cancer. This paper presents a contribution that aims to optimize the detection of protein commu-nities through a greedy algorithm that is implemented in the C programming language. The optimization involves a double improvement in relation to pro-tein communities detection, which is accomplished both at the algorithmic and programming level. The resulting implementation's performance was carefully tested on real biological data and the results acknowledge the relevant speedup that the optimization determines. Moreover, the results are in line with the previous findings that our current research produced, as it reveals and confirms the existence of some important properties of those proteins that participate in the carcinogenesis process. Apart from being particularly useful for research purposes, the novel community detection algorithm also dramatically speeds up the proteomic databases analysis process, as compared to some other se-quential community detection approaches, and also to the sequential algorithm of Newman and Girvan.

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    ABSTRACT: Proteins and the networks they determine, called interactome networks, have received attention at an important degree during the last years, because they have been discovered to have an influence on some complex biological phenomena, such as problematic disorders like cancer. This paper describes an optimized algorithm, which assesses the importance of proteins that belong to an interactome network. Thus, it computes the impor-tance of each protein through the usage of an adapted Dijkstra-based betweenness computation algorithm. The procedure allows for an informative and comprehensive analysis of interactome networks to be conducted, at var-ious levels of complexity. The practical performance and usefulness of the algorithm was carefully tested on real biological data and the results acknowledge it is able to process existing proteomic data in a timely and informa-tive manner, considering existing one-processor hardware architectures. Apart from being particularly useful for research purposes, the novel protein data analysis method also dramatically speeds up the proteomic databases analysis process.
    01/2011;

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