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10.1093/jnci/djw146 http://scihub22266oqcxt.onion/10.1093/jnci/djw146 C5241903!5241903!27381624     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=27381624&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 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       J+Natl+Cancer+Inst 2016 ; 108 (11): ä Nephropedia Template TP {{tp|p=27381624|t=2016. The Doppelgänger Effect: Hidden Duplicates in Databases of Transcriptome Profiles |pdf=|usr=}}{{27381624}} gab.com Text The Doppelgänger Effect: Hidden Duplicates in Databases of Transcriptome Profiles JO: J Natl Cancer Inst http://www.kidney.de/pget.php?&tw=1&pmid=27381624 #FOAvip Whole-genome analysis of cancer specimens is commonplace, and investigators frequently share or re-use specimens in later studies. Duplicate expression profiles in public databases will impact re-analysis if left undetected, a so-called ?doppelgänger? effect. We propose a method that should be routine practice to accurately match duplicate cancer transcriptomes when nucleotide-level sequence data are unavailable, even for samples profiled by different microarray technologies or by both microarray and RNA sequencing. We demonstrate the effectiveness of the method in databases containing dozens of datasets and thousands of ovarian, breast, bladder, and colorectal cancer microarray profiles and of matching microarray and RNA sequencing expression profiles from The Cancer Genome Atlas (TCGA). We identified probable duplicates among more than 50% of studies, originating in different continents, using different technologies, published years apart, and even within the TCGA itself. Finally, we provide the doppelgangR Bioconductor package for screening transcriptome databases for duplicates. Given the potential for unrecognized duplication to falsely inflate prediction accuracy and confidence in differential expression, doppelgänger-checking should be a part of standard procedure for combining multiple genomic datasets. Twit Text FOAVip The Doppelgänger Effect: Hidden Duplicates in Databases of Transcriptome Profiles ????J Natl Cancer Inst http://www.kidney.de/pget.php?&tw=1&pmid=27381624 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27381624 #FOAvip English Wikipedia <ref>{{Cite pmid|27381624}}</ref> The Doppelgänger Effect: Hidden Duplicates in Databases of Transcriptome Profiles #MMPMID27381624 Waldron L; Riester M; Ramos M; Parmigiani G; Birrer M J Natl Cancer Inst 2016[Nov]; 108 (11): ä PMID27381624show ga Whole-genome analysis of cancer specimens is commonplace, and investigators frequently share or re-use specimens in later studies. Duplicate expression profiles in public databases will impact re-analysis if left undetected, a so-called ?doppelgänger? effect. We propose a method that should be routine practice to accurately match duplicate cancer transcriptomes when nucleotide-level sequence data are unavailable, even for samples profiled by different microarray technologies or by both microarray and RNA sequencing. We demonstrate the effectiveness of the method in databases containing dozens of datasets and thousands of ovarian, breast, bladder, and colorectal cancer microarray profiles and of matching microarray and RNA sequencing expression profiles from The Cancer Genome Atlas (TCGA). We identified probable duplicates among more than 50% of studies, originating in different continents, using different technologies, published years apart, and even within the TCGA itself. Finally, we provide the doppelgangR Bioconductor package for screening transcriptome databases for duplicates. Given the potential for unrecognized duplication to falsely inflate prediction accuracy and confidence in differential expression, doppelgänger-checking should be a part of standard procedure for combining multiple genomic datasets. äThe Doppelgänger Effect: Hidden Duplicates in Databases of Transcripto(epmc).pdf DeepDyve Pubget Overpricing