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10.1155/2015/462549 http://scihub22266oqcxt.onion/10.1155/2015/462549 C4539210!4539210!26339613     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26339613&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 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biomed+Res+Int 2015 ; 2015 (ä): ä Nephropedia Template TP {{tp|p=26339613|t=2015. Identifying and Assessing Interesting Subgroups in a Heterogeneous Population |pdf=|usr=}}{{26339613}} gab.com Text Identifying and Assessing Interesting Subgroups in a Heterogeneous Population JO: Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=26339613 #FOAvip Biological heterogeneity is common in many diseases and it is often the reason for therapeutic failures. Thus, there is great interest in classifying a disease into subtypes that have clinical significance in terms of prognosis or therapy response. One of the most popular methods to uncover unrecognized subtypes is cluster analysis. However, classical clustering methods such as k-means clustering or hierarchical clustering are not guaranteed to produce clinically interesting subtypes. This could be because the main statistical variability?the basis of cluster generation?is dominated by genes not associated with the clinical phenotype of interest. Furthermore, a strong prognostic factor might be relevant for a certain subgroup but not for the whole population; thus an analysis of the whole sample may not reveal this prognostic factor. To address these problems we investigate methods to identify and assess clinically interesting subgroups in a heterogeneous population. The identification step uses a clustering algorithm and to assess significance we use a false discovery rate- (FDR-) based measure. Under the heterogeneity condition the standard FDR estimate is shown to overestimate the true FDR value, but this is remedied by an improved FDR estimation procedure. As illustrations, two real data examples from gene expression studies of lung cancer are provided. Twit Text FOAVip Identifying and Assessing Interesting Subgroups in a Heterogeneous Population ????Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=26339613 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26339613 #FOAvip English Wikipedia <ref>{{Cite pmid|26339613}}</ref> Identifying and Assessing Interesting Subgroups in a Heterogeneous Population #MMPMID26339613 Lee W; Alexeyenko A; Pernemalm M; Guegan J; Dessen P; Lazar V; Lehtiö J; Pawitan Y Biomed Res Int 2015[]; 2015 (ä): ä PMID26339613show ga Biological heterogeneity is common in many diseases and it is often the reason for therapeutic failures. Thus, there is great interest in classifying a disease into subtypes that have clinical significance in terms of prognosis or therapy response. One of the most popular methods to uncover unrecognized subtypes is cluster analysis. However, classical clustering methods such as k-means clustering or hierarchical clustering are not guaranteed to produce clinically interesting subtypes. This could be because the main statistical variability?the basis of cluster generation?is dominated by genes not associated with the clinical phenotype of interest. Furthermore, a strong prognostic factor might be relevant for a certain subgroup but not for the whole population; thus an analysis of the whole sample may not reveal this prognostic factor. To address these problems we investigate methods to identify and assess clinically interesting subgroups in a heterogeneous population. The identification step uses a clustering algorithm and to assess significance we use a false discovery rate- (FDR-) based measure. Under the heterogeneity condition the standard FDR estimate is shown to overestimate the true FDR value, but this is remedied by an improved FDR estimation procedure. As illustrations, two real data examples from gene expression studies of lung cancer are provided. äIdentifying and Assessing Interesting Subgroups in a Heterogeneous Pop(epmc).pdf DeepDyve Pubget Overpricing