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10.1093/nar/gkx291 http://scihub22266oqcxt.onion/10.1093/nar/gkx291 C5499546!5499546 !28472344     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=28472344 &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 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 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\28472344 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Nucleic+Acids+Res 2017 ; 45 (12 ): e114 Nephropedia Template TP {{tp|p=28472344 |t=2017. Gene set selection via LASSO penalized regression (SLPR) |pdf=|usr=}}{{28472344 }} gab.com Text Gene set selection via LASSO penalized regression (SLPR) JO: Nucleic Acids Res http://www.kidney.de/pget.php?&tw=1&pmid=28472344 #FOAvip Gene set testing is an important bioinformatics technique that addresses the challenges of power, interpretation and replication. To better support the analysis of large and highly overlapping gene set collections, researchers have recently developed a number of multiset methods that jointly evaluate all gene sets in a collection to identify a parsimonious group of functionally independent sets. Unfortunately, current multiset methods all use binary indicators for gene and gene set activity and assume that a gene is active if any containing gene set is active. This simplistic model limits performance on many types of genomic data. To address this limitation, we developed gene set Selection via LASSO Penalized Regression (SLPR), a novel mapping of multiset gene set testing to penalized multiple linear regression. The SLPR method assumes a linear relationship between continuous measures of gene activity and the activity of all gene sets in the collection. As we demonstrate via simulation studies and the analysis of TCGA data using MSigDB gene sets, the SLPR method outperforms existing multiset methods when the true biological process is well approximated by continuous activity measures and a linear association between genes and gene sets. Twit Text FOAVip Gene set selection via LASSO penalized regression (SLPR) ????Nucleic Acids Res http://www.kidney.de/pget.php?&tw=1&pmid=28472344 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28472344 #FOAvip English Wikipedia <ref>{{Cite pmid|28472344 }}</ref> Gene set selection via LASSO penalized regression (SLPR) #MMPMID28472344 Frost HR ; Amos CI Nucleic Acids Res 2017[Jul]; 45 (12 ): e114 PMID28472344 show ga Gene set testing is an important bioinformatics technique that addresses the challenges of power, interpretation and replication. To better support the analysis of large and highly overlapping gene set collections, researchers have recently developed a number of multiset methods that jointly evaluate all gene sets in a collection to identify a parsimonious group of functionally independent sets. Unfortunately, current multiset methods all use binary indicators for gene and gene set activity and assume that a gene is active if any containing gene set is active. This simplistic model limits performance on many types of genomic data. To address this limitation, we developed gene set Selection via LASSO Penalized Regression (SLPR), a novel mapping of multiset gene set testing to penalized multiple linear regression. The SLPR method assumes a linear relationship between continuous measures of gene activity and the activity of all gene sets in the collection. As we demonstrate via simulation studies and the analysis of TCGA data using MSigDB gene sets, the SLPR method outperforms existing multiset methods when the true biological process is well approximated by continuous activity measures and a linear association between genes and gene sets. |*Gene Expression Regulation, Neoplastic [MESH] |*Models, Statistical [MESH] |Adenocarcinoma of Lung [MESH] |Adenocarcinoma/diagnosis/*genetics/pathology [MESH] |Benchmarking [MESH] |Biomarkers, Tumor/*genetics [MESH] |Carcinoma, Squamous Cell/diagnosis/*genetics/pathology [MESH] |Computational Biology/statistics & numerical data [MESH] |Diagnosis, Differential [MESH] |Humans [MESH] |Lung Neoplasms/diagnosis/*genetics/pathology [MESH] |Metabolic Networks and Pathways/genetics [MESH] |Models, Biological [MESH] |Multigene Family [MESH] |Neoplasm Proteins/*genetics [MESH]Gene set selection via LASSO penalized regression (SLPR) (epmc).pdf DeepDyve Pubget Overpricing