Yizhen Shen

Donghua University, Shanghai, Shanghai Shi, China

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Publications (3)0 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: Membrane protein and its interaction network have become a novel research direction in bioinformatics. In this paper, a novel membrane protein interaction network simulator is proposed for system biology studies by integrated intelligence method including spectrum analysis, fuzzy K-Nearest Neighbor(KNN) algorithm and so on. We consider biological system as a set of active computational components interacting with each other and with the external environment. Then we can use the network simulator to construct membrane protein interaction networks. Based on the proposed approach, we found that the membrane protein interaction network almost has some dynamic and collective characteristics, such as small-world network, scale free distributing, and hierarchical module structure. These properties are similar to those of other extensively studied protein interaction networks. The present studies on the characteristics of the membrane protein interaction network will be valuable for its relatively biological and medical studies.
    Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi 08/2011; 28(4):658-62.
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    ABSTRACT: G-protein-coupled receptors (GPCRs), the largest family of cell surface receptors, play an important role in the production of therapeutic drugs. The functions of GPCRs are closely related to their classification and subclassification. It is difficult to obtain the spatial structure of GPCRs sequence by experimental approaches. It is highly desired to develop powerful tools and effective algorithms for classifying the family of GPCRs. In this study, based on the concept of pseudo amino acid composition (PseAA) originally introduced by Chou, approximate entropy (ApEn) of protein sequence as an additional characteristic is used to construct PseAA. A 21-D (dimensional) PseAA is formulated to represent the sample of a protein. Fuzzy K nearest neighbors (FKNN) classifier is adopted as prediction engine. The datasets in low homology are used to validate the performance of the proposed method. Compared with prior works, the successful rates achieved of our research are the highest. The test results indicate that the novel approach can play the role of a compliment to many of the existing methods, which promises to be a useful tool for GPCRs function prediction.
    Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi 06/2010; 27(3):500-4.
  • Yizhen Shen, Yongsheng Ding, Quan Gu
    International Conference on Pattern Recognition in Bioinformatics; 10/2008