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.

10.1186/s13326-017-0125-1 http://scihub22266oqcxt.onion/10.1186/s13326-017-0125-1 C5399403!5399403!28427468     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       J+Biomed+Semantics 2017 ; 8 (ä): ä Nephropedia Template TP {{tp|p=28427468|t=2017. Learning from biomedical linked data to suggest valid pharmacogenes |pdf=|usr=}}{{28427468}} gab.com Text Learning from biomedical linked data to suggest valid pharmacogenes JO: J Biomed Semantics http://www.kidney.de/pget.php?&tw=1&pmid=28427468 #FOAvip Background: A standard task in pharmacogenomics research is identifying genes that may be involved in drug response variability, i.e., pharmacogenes. Because genomic experiments tended to generate many false positives, computational approaches based on the use of background knowledge have been proposed. Until now, only molecular networks or the biomedical literature were used, whereas many other resources are available. Method: We propose here to consume a diverse and larger set of resources using linked data related either to genes, drugs or diseases. One of the advantages of linked data is that they are built on a standard framework that facilitates the joint use of various sources, and thus facilitates considering features of various origins. We propose a selection and linkage of data sources relevant to pharmacogenomics, including for example DisGeNET and Clinvar. We use machine learning to identify and prioritize pharmacogenes that are the most probably valid, considering the selected linked data. This identification relies on the classification of gene?drug pairs as either pharmacogenomically associated or not and was experimented with two machine learning methods ?random forest and graph kernel?, which results are compared in this article. Results: We assembled a set of linked data relative to pharmacogenomics, of 2,610,793 triples, coming from six distinct resources. Learning from these data, random forest enables identifying valid pharmacogenes with a F-measure of 0.73, on a 10 folds cross-validation, whereas graph kernel achieves a F-measure of 0.81. A list of top candidates proposed by both approaches is provided and their obtention is discussed. Electronic supplementary material: The online version of this article (doi:10.1186/s13326-017-0125-1) contains supplementary material, which is available to authorized users. Twit Text FOAVip Learning from biomedical linked data to suggest valid pharmacogenes ????J Biomed Semantics http://www.kidney.de/pget.php?&tw=1&pmid=28427468 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28427468 #FOAvip English Wikipedia <ref>{{Cite pmid|28427468}}</ref> Learning from biomedical linked data to suggest valid pharmacogenes #MMPMID28427468 Dalleau K; Marzougui Y; Da Silva S; Ringot P; Ndiaye NC; Coulet A J Biomed Semantics 2017[]; 8 (ä): ä PMID28427468show ga Background: A standard task in pharmacogenomics research is identifying genes that may be involved in drug response variability, i.e., pharmacogenes. Because genomic experiments tended to generate many false positives, computational approaches based on the use of background knowledge have been proposed. Until now, only molecular networks or the biomedical literature were used, whereas many other resources are available. Method: We propose here to consume a diverse and larger set of resources using linked data related either to genes, drugs or diseases. One of the advantages of linked data is that they are built on a standard framework that facilitates the joint use of various sources, and thus facilitates considering features of various origins. We propose a selection and linkage of data sources relevant to pharmacogenomics, including for example DisGeNET and Clinvar. We use machine learning to identify and prioritize pharmacogenes that are the most probably valid, considering the selected linked data. This identification relies on the classification of gene?drug pairs as either pharmacogenomically associated or not and was experimented with two machine learning methods ?random forest and graph kernel?, which results are compared in this article. Results: We assembled a set of linked data relative to pharmacogenomics, of 2,610,793 triples, coming from six distinct resources. Learning from these data, random forest enables identifying valid pharmacogenes with a F-measure of 0.73, on a 10 folds cross-validation, whereas graph kernel achieves a F-measure of 0.81. A list of top candidates proposed by both approaches is provided and their obtention is discussed. Electronic supplementary material: The online version of this article (doi:10.1186/s13326-017-0125-1) contains supplementary material, which is available to authorized users. äLearning from biomedical linked data to suggest valid pharmacogenes (epmc).pdf DeepDyve Pubget Overpricing