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10.1186/s12859-016-1115-5 http://scihub22266oqcxt.onion/10.1186/s12859-016-1115-5 C5009824!5009824!27586883     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       BMC+Bioinformatics 2016 ; 17 (Suppl 8): ä Nephropedia Template TP {{tp|p=27586883|t=2016. Predicting essential proteins based on subcellular localization, orthology and PPI networks |pdf=|usr=}}{{27586883}} gab.com Text Predicting essential proteins based on subcellular localization, orthology and PPI networks JO: BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=27586883 #FOAvip Background: Essential proteins play an indispensable role in the cellular survival and development. There have been a series of biological experimental methods for finding essential proteins; however they are time-consuming, expensive and inefficient. In order to overcome the shortcomings of biological experimental methods, many computational methods have been proposed to predict essential proteins. The computational methods can be roughly divided into two categories, the topology-based methods and the sequence-based ones. The former use the topological features of protein-protein interaction (PPI) networks while the latter use the sequence features of proteins to predict essential proteins. Nevertheless, it is still challenging to improve the prediction accuracy of the computational methods. Results: Comparing with nonessential proteins, essential proteins appear more frequently in certain subcellular locations and their evolution more conservative. By integrating the information of subcellular localization, orthologous proteins and PPI networks, we propose a novel essential protein prediction method, named SON, in this study. The experimental results on S.cerevisiae data show that the prediction accuracy of SON clearly exceeds that of nine competing methods: DC, BC, IC, CC, SC, EC, NC, PeC and ION. Conclusions: We demonstrate that, by integrating the information of subcellular localization, orthologous proteins with PPI networks, the accuracy of predicting essential proteins can be improved. Our proposed method SON is effective for predicting essential proteins. Twit Text FOAVip Predicting essential proteins based on subcellular localization, orthology and PPI networks ????BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=27586883 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27586883 #FOAvip English Wikipedia <ref>{{Cite pmid|27586883}}</ref> Predicting essential proteins based on subcellular localization, orthology and PPI networks #MMPMID27586883 Li G; Li M; Wang J; Wu J; Wu FX; Pan Y BMC Bioinformatics 2016[]; 17 (Suppl 8): ä PMID27586883show ga Background: Essential proteins play an indispensable role in the cellular survival and development. There have been a series of biological experimental methods for finding essential proteins; however they are time-consuming, expensive and inefficient. In order to overcome the shortcomings of biological experimental methods, many computational methods have been proposed to predict essential proteins. The computational methods can be roughly divided into two categories, the topology-based methods and the sequence-based ones. The former use the topological features of protein-protein interaction (PPI) networks while the latter use the sequence features of proteins to predict essential proteins. Nevertheless, it is still challenging to improve the prediction accuracy of the computational methods. Results: Comparing with nonessential proteins, essential proteins appear more frequently in certain subcellular locations and their evolution more conservative. By integrating the information of subcellular localization, orthologous proteins and PPI networks, we propose a novel essential protein prediction method, named SON, in this study. The experimental results on S.cerevisiae data show that the prediction accuracy of SON clearly exceeds that of nine competing methods: DC, BC, IC, CC, SC, EC, NC, PeC and ION. Conclusions: We demonstrate that, by integrating the information of subcellular localization, orthologous proteins with PPI networks, the accuracy of predicting essential proteins can be improved. Our proposed method SON is effective for predicting essential proteins. äPredicting essential proteins based on subcellular localization, ortho(epmc).pdf DeepDyve Pubget Overpricing