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.

10.1093/bioinformatics/btu265 http://scihub22266oqcxt.onion/10.1093/bioinformatics/btu265 C4058936!4058936 !24931980     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 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 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\24931980 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Bioinformatics 2014 ; 30 (12 ): i165-74 Nephropedia Template TP {{tp|p=24931980 |t=2014. Metabolome-scale prediction of intermediate compounds in multistep metabolic pathways with a recursive supervised approach |pdf=|usr=}}{{24931980 }} gab.com Text Metabolome-scale prediction of intermediate compounds in multistep metabolic pathways with a recursive supervised approach JO: Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=24931980 #FOAvip MOTIVATION: Metabolic pathway analysis is crucial not only in metabolic engineering but also in rational drug design. However, the biosynthetic/biodegradation pathways are known only for a small portion of metabolites, and a vast amount of pathways remain uncharacterized. Therefore, an important challenge in metabolomics is the de novo reconstruction of potential reaction networks on a metabolome-scale. RESULTS: In this article, we develop a novel method to predict the multistep reaction sequences for de novo reconstruction of metabolic pathways in the reaction-filling framework. We propose a supervised approach to learn what we refer to as 'multistep reaction sequence likeness', i.e. whether a compound-compound pair is possibly converted to each other by a sequence of enzymatic reactions. In the algorithm, we propose a recursive procedure of using step-specific classifiers to predict the intermediate compounds in the multistep reaction sequences, based on chemical substructure fingerprints/descriptors of compounds. We further demonstrate the usefulness of our proposed method on the prediction of enzymatic reaction networks from a metabolome-scale compound set and discuss characteristic features of the extracted chemical substructure transformation patterns in multistep reaction sequences. Our comprehensively predicted reaction networks help to fill the metabolic gap and to infer new reaction sequences in metabolic pathways. AVAILABILITY AND IMPLEMENTATION: Materials are available for free at http://web.kuicr.kyoto-u.ac.jp/supp/kot/ismb2014/ Twit Text FOAVip Metabolome-scale prediction of intermediate compounds in multistep metabolic pathways with a recursive supervised approach ????Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=24931980 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24931980 #FOAvip English Wikipedia <ref>{{Cite pmid|24931980 }}</ref> Metabolome-scale prediction of intermediate compounds in multistep metabolic pathways with a recursive supervised approach #MMPMID24931980 Kotera M ; Tabei Y ; Yamanishi Y ; Muto A ; Moriya Y ; Tokimatsu T ; Goto S Bioinformatics 2014[Jun]; 30 (12 ): i165-74 PMID24931980 show ga MOTIVATION: Metabolic pathway analysis is crucial not only in metabolic engineering but also in rational drug design. However, the biosynthetic/biodegradation pathways are known only for a small portion of metabolites, and a vast amount of pathways remain uncharacterized. Therefore, an important challenge in metabolomics is the de novo reconstruction of potential reaction networks on a metabolome-scale. RESULTS: In this article, we develop a novel method to predict the multistep reaction sequences for de novo reconstruction of metabolic pathways in the reaction-filling framework. We propose a supervised approach to learn what we refer to as 'multistep reaction sequence likeness', i.e. whether a compound-compound pair is possibly converted to each other by a sequence of enzymatic reactions. In the algorithm, we propose a recursive procedure of using step-specific classifiers to predict the intermediate compounds in the multistep reaction sequences, based on chemical substructure fingerprints/descriptors of compounds. We further demonstrate the usefulness of our proposed method on the prediction of enzymatic reaction networks from a metabolome-scale compound set and discuss characteristic features of the extracted chemical substructure transformation patterns in multistep reaction sequences. Our comprehensively predicted reaction networks help to fill the metabolic gap and to infer new reaction sequences in metabolic pathways. AVAILABILITY AND IMPLEMENTATION: Materials are available for free at http://web.kuicr.kyoto-u.ac.jp/supp/kot/ismb2014/ |*Metabolic Networks and Pathways [MESH] |*Metabolome [MESH] |Algorithms [MESH] |Metabolomics/*methods [MESH]Metabolome-scale prediction of intermediate compounds in multistep met(epmc).pdf DeepDyve Pubget Overpricing