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    Defining the protein interaction network of human malaria parasite Plasmodium falciparum

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    Type
    Article
    Authors
    Ramaprasad, Abhinay cc
    Pain, Arnab cc
    Ravasi, Timothy cc
    KAUST Department
    Applied Mathematics and Computational Science Program
    Biological and Environmental Sciences and Engineering (BESE) Division
    Bioscience Program
    Computational Bioscience Research Center (CBRC)
    Date
    2012-02
    Permanent link to this record
    http://hdl.handle.net/10754/565967
    
    Metadata
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    Abstract
    Malaria, caused by the protozoan parasite Plasmodium falciparum, affects around 225. million people yearly and a huge international effort is directed towards combating this grave threat to world health and economic development. Considerable advances have been made in malaria research triggered by the sequencing of its genome in 2002, followed by several high-throughput studies defining the malaria transcriptome and proteome. A protein-protein interaction (PPI) network seeks to trace the dynamic interactions between proteins, thereby elucidating their local and global functional relationships. Experimentally derived PPI network from high-throughput methods such as yeast two hybrid (Y2H) screens are inherently noisy, but combining these independent datasets by computational methods tends to give a greater accuracy and coverage. This review aims to discuss the computational approaches used till date to construct a malaria protein interaction network and to catalog the functional predictions and biological inferences made from analysis of the PPI network. © 2011 Elsevier Inc.
    Sponsors
    This work was funded by King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia.
    Publisher
    Elsevier BV
    Journal
    Genomics
    DOI
    10.1016/j.ygeno.2011.11.006
    PubMed ID
    22178265
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.ygeno.2011.11.006
    Scopus Count
    Collections
    Articles; Biological and Environmental Sciences and Engineering (BESE) Division; Bioscience Program; Applied Mathematics and Computational Science Program; Computational Bioscience Research Center (CBRC)

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