Optimizing Filter Processes on Protein Interaction Clustering Results Using Genetic Algorithms

Abstract : In this manuscript, a Genetic Algorithm is applied on a filter in order to optimize the selection of clusters having a high probability to represent protein complexes. The filter was applied on the results (obtained by experiments made on five different yeast datasets) of three different algorithms known for their efficiency on protein complex detection through protein interaction graphs. The derived results were compared with three popular clustering algorithms, proving the efficiency of the proposed method according to metrics such as successful prediction rate and geometrical accuracy.
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Communication dans un congrès
Lazaros Iliadis; Chrisina Jayne. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-363 (Part I), pp.463-470, 2011, Engineering Applications of Neural Networks. 〈10.1007/978-3-642-23957-1_51〉
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Charalampos Moschopoulos, Grigorios Beligiannis, Sophia Kossida, Spiridon Likothanassis. Optimizing Filter Processes on Protein Interaction Clustering Results Using Genetic Algorithms. Lazaros Iliadis; Chrisina Jayne. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-363 (Part I), pp.463-470, 2011, Engineering Applications of Neural Networks. 〈10.1007/978-3-642-23957-1_51〉. 〈hal-01571342〉

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