Abstract
Proteins and their interactions have been proven to play a central role in many cellular processes. Although there are many experimental techniques for protein-protein interaction prediction, only a few exist for predicting protein complexes. For the sake of this, researchers have emphasized lately in the computational prediction of protein complexes from Protein-Protein Interaction (PPI) data. The two major limitations of the current advances in the prediction of protein complexes are that most of the algorithms do not take into consideration the participation of a protein to many protein complexes and that they cannot handle weighted PPI graphs. In the present paper, we altered the original Restricted Neighborhood Search Clustering (RNSC) algorithm to overcome the above limitations. The Enhanced Weighted Restricted Neighborhood Search Clustering (EWRNSC) permits the participation of a protein to many protein complexes by modifying the moves of the original RNSC. In addition, EWRNSC can accept and process weighted PPI graphs as inputs by altering the cost functions of the original RNSC cost clustering schemes. When experimented using atasets from Human, the proposed algorithm proved to outperform the original RNSC and the MCL algorithms which are two of the most broadly used methods in the field of protein complexes prediction.
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Dimitrakopoulos, C., Theofilatos, K., Pegkas, A., Likothanassis, S., Mavroudi, S. (2013). Enhanced Weighted Restricted Neighborhood Search Clustering: A Novel Algorithm for Detecting Human Protein Complexes from Weighted Protein-Protein Interaction Graphs. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2013. Communications in Computer and Information Science, vol 384. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41016-1_25
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DOI: https://doi.org/10.1007/978-3-642-41016-1_25
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