Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1155/2016/5813645 http://scihub22266oqcxt.onion/10.1155/2016/5813645 C4875977!4875977!27294123     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biomed+Res+Int 2016 ; 2016 (ä): ä Nephropedia Template TP {{tp|p=27294123|t=2016. Protein Remote Homology Detection Based on an Ensemble Learning Approach |pdf=|usr=}}{{27294123}} gab.com Text Protein Remote Homology Detection Based on an Ensemble Learning Approach JO: Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=27294123 #FOAvip Protein remote homology detection is one of the central problems in bioinformatics. Although some computational methods have been proposed, the problem is still far from being solved. In this paper, an ensemble classifier for protein remote homology detection, called SVM-Ensemble, was proposed with a weighted voting strategy. SVM-Ensemble combined three basic classifiers based on different feature spaces, including Kmer, ACC, and SC-PseAAC. These features consider the characteristics of proteins from various perspectives, incorporating both the sequence composition and the sequence-order information along the protein sequences. Experimental results on a widely used benchmark dataset showed that the proposed SVM-Ensemble can obviously improve the predictive performance for the protein remote homology detection. Moreover, it achieved the best performance and outperformed other state-of-the-art methods. Twit Text FOAVip Protein Remote Homology Detection Based on an Ensemble Learning Approach ????Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=27294123 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27294123 #FOAvip English Wikipedia <ref>{{Cite pmid|27294123}}</ref> Protein Remote Homology Detection Based on an Ensemble Learning Approach #MMPMID27294123 Chen J; Liu B; Huang D Biomed Res Int 2016[]; 2016 (ä): ä PMID27294123show ga Protein remote homology detection is one of the central problems in bioinformatics. Although some computational methods have been proposed, the problem is still far from being solved. In this paper, an ensemble classifier for protein remote homology detection, called SVM-Ensemble, was proposed with a weighted voting strategy. SVM-Ensemble combined three basic classifiers based on different feature spaces, including Kmer, ACC, and SC-PseAAC. These features consider the characteristics of proteins from various perspectives, incorporating both the sequence composition and the sequence-order information along the protein sequences. Experimental results on a widely used benchmark dataset showed that the proposed SVM-Ensemble can obviously improve the predictive performance for the protein remote homology detection. Moreover, it achieved the best performance and outperformed other state-of-the-art methods. äProtein Remote Homology Detection Based on an Ensemble Learning Approa(epmc).pdf DeepDyve Pubget Overpricing