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10.1155/2014/375262 http://scihub22266oqcxt.onion/10.1155/2014/375262 C4052612!4052612!24963481     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biomed+Res+Int 2014 ; 2014 (ä): ä Nephropedia Template TP {{tp|p=24963481|t=2014. Identifying Dynamic Protein Complexes Based on Gene Expression Profiles and PPI Networks |pdf=|usr=}}{{24963481}} gab.com Text Identifying Dynamic Protein Complexes Based on Gene Expression Profiles and PPI Networks JO: Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=24963481 #FOAvip Identification of protein complexes from protein-protein interaction networks has become a key problem for understanding cellular life in postgenomic era. Many computational methods have been proposed for identifying protein complexes. Up to now, the existing computational methods are mostly applied on static PPI networks. However, proteins and their interactions are dynamic in reality. Identifying dynamic protein complexes is more meaningful and challenging. In this paper, a novel algorithm, named DPC, is proposed to identify dynamic protein complexes by integrating PPI data and gene expression profiles. According to Core-Attachment assumption, these proteins which are always active in the molecular cycle are regarded as core proteins. The protein-complex cores are identified from these always active proteins by detecting dense subgraphs. Final protein complexes are extended from the protein-complex cores by adding attachments based on a topological character of ?closeness? and dynamic meaning. The protein complexes produced by our algorithm DPC contain two parts: static core expressed in all the molecular cycle and dynamic attachments short-lived. The proposed algorithm DPC was applied on the data of Saccharomyces cerevisiae and the experimental results show that DPC outperforms CMC, MCL, SPICi, HC-PIN, COACH, and Core-Attachment based on the validation of matching with known complexes and hF-measures. Twit Text FOAVip Identifying Dynamic Protein Complexes Based on Gene Expression Profiles and PPI Networks ????Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=24963481 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24963481 #FOAvip English Wikipedia <ref>{{Cite pmid|24963481}}</ref> Identifying Dynamic Protein Complexes Based on Gene Expression Profiles and PPI Networks #MMPMID24963481 Li M; Chen W; Wang J; Wu FX; Pan Y Biomed Res Int 2014[]; 2014 (ä): ä PMID24963481show ga Identification of protein complexes from protein-protein interaction networks has become a key problem for understanding cellular life in postgenomic era. Many computational methods have been proposed for identifying protein complexes. Up to now, the existing computational methods are mostly applied on static PPI networks. However, proteins and their interactions are dynamic in reality. Identifying dynamic protein complexes is more meaningful and challenging. In this paper, a novel algorithm, named DPC, is proposed to identify dynamic protein complexes by integrating PPI data and gene expression profiles. According to Core-Attachment assumption, these proteins which are always active in the molecular cycle are regarded as core proteins. The protein-complex cores are identified from these always active proteins by detecting dense subgraphs. Final protein complexes are extended from the protein-complex cores by adding attachments based on a topological character of ?closeness? and dynamic meaning. The protein complexes produced by our algorithm DPC contain two parts: static core expressed in all the molecular cycle and dynamic attachments short-lived. The proposed algorithm DPC was applied on the data of Saccharomyces cerevisiae and the experimental results show that DPC outperforms CMC, MCL, SPICi, HC-PIN, COACH, and Core-Attachment based on the validation of matching with known complexes and hF-measures. äIdentifying Dynamic Protein Complexes Based on Gene Expression Profile(epmc).pdf DeepDyve Pubget Overpricing