Saeed Omidi

University of Tehran, Teheran, Tehrān, Iran

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Publications (4)0.93 Total impact

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    H. Ravaee · A. Masoudi-Nejad · S. Omidi · A. Moeini ·
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    ABSTRACT: Clustering protein-protein interaction network aims to find functional modules and protein complexes. There are many computational graph clustering methods that are used in this field, but few of them are intelligent computational methods. In this paper, we present a novel improved immune genetic algorithm to find dense subgraphs based on efficient vaccination method, variable-length antibody schema definition and new local and global mutations.
    BioInformatics and BioEngineering (BIBE), 2010 IEEE International Conference on; 07/2010
  • Saeed Omidi · Ali Masoudi-Nejad ·
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    ABSTRACT: In this chapter, we intend to give a review on some of the important network models that are introduced in recent years. The aim of all of these models is to imitate the real-world network properties. Real-world networks exhibit behaviors such as small-world, scale-free, and high clustering coefficient. One of the significant models known as Barabási–Albert model utilizes preferential attachment mechanism as a main mechanism for power-law networks generation. Ubiquity of preferential attachment in network evolution has been proved for many kinds of networks. Additionally, one can generalize functional form of the preferential attachment mathematically, where it provides three different regimes. Besides, in real-world networks, there exist natural constraints such as age or cost that one can consider; however, all of these models are classified as global models. Another important family of models that rely on local strategies attempt to realize network evolution mechanism. These models generate power-law network through making decisions based on the local properties of the networks.
    12/2009: pages 191-240;
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    Saeed Omidi · Falk Schreiber · Ali Masoudi-Nejad ·
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    ABSTRACT: In recent years, interest has been growing in the study of complex networks. Since Erdös and Rényi (1960) proposed their random graph model about 50 years ago, many researchers have investigated and shaped this field. Many indicators have been proposed to assess the global features of networks. Recently, an active research area has developed in studying local features named motifs as the building blocks of networks. Unfortunately, network motif discovery is a computationally hard problem and finding rather large motifs (larger than 8 nodes) by means of current algorithms is impractical as it demands too much computational effort. In this paper, we present a new algorithm (MODA) that incorporates techniques such as a pattern growth approach for extracting larger motifs efficiently. We have tested our algorithm and found it able to identify larger motifs with more than 8 nodes more efficiently than most of the current state-of-the-art motif discovery algorithms. While most of the algorithms rely on induced subgraphs as motifs of the networks, MODA is able to extract both induced and non-induced subgraphs simultaneously. The MODA source code is freely available at:
    Genes & Genetic Systems 01/2009; 84(5):385-95. DOI:10.1266/ggs.84.385 · 0.93 Impact Factor
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