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10.1007/978-1-60327-563-7_9 http://scihub22266oqcxt.onion/10.1007/978-1-60327-563-7_9 C4667951!4667951!19588106     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       Methods+Mol+Biol 2009 ; 553 (ä): 181-206 Nephropedia Template TP {{tp|p=19588106|t=2009. Challenges and Approaches to Statistical Design and Inference in High Dimensional Investigations |pdf=|usr=}}{{19588106}} gab.com Text Challenges and Approaches to Statistical Design and Inference in High Dimensional Investigations JO: Methods Mol Biol http://www.kidney.de/pget.php?&tw=1&pmid=19588106 #FOAvip Advances in modern technologies have facilitated high-dimensional experiments (HDEs) that generate tremendous amounts of genomic, proteomic, and other ?omic? data. HDEs involving whole-genome sequences and polymorphisms, expression levels of genes, protein abundance measurements, and combinations thereof have become a vanguard for new analytic approaches to the analysis of HDE data. Such situations demand creative approaches to the processes of statistical inference, estimation, prediction, classification, and study design. The novel and challenging biological questions asked from HDE data have resulted in many specialized analytic techniques being developed. This chapter discusses some of the unique statistical challenges facing investigators studying high-dimensional biology, and describes some approaches being developed by statistical scientists. We have included some focus on the increasing interest in questions involving testing multiple propositions simultaneously, appropriate inferential indicators for the types of questions biologists are interested in, and the need for replication of results across independent studies, investigators, and settings. A key consideration inherent throughout is the challenge in providing methods that a statistician judges to be sound and a biologist finds informative. Twit Text FOAVip Challenges and Approaches to Statistical Design and Inference in High Dimensional Investigations ????Methods Mol Biol http://www.kidney.de/pget.php?&tw=1&pmid=19588106 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=19588106 #FOAvip English Wikipedia <ref>{{Cite pmid|19588106}}</ref> Challenges and Approaches to Statistical Design and Inference in High Dimensional Investigations #MMPMID19588106 Gadbury GL; Garrett KA; Allison DB Methods Mol Biol 2009[]; 553 (ä): 181-206 PMID19588106show ga Advances in modern technologies have facilitated high-dimensional experiments (HDEs) that generate tremendous amounts of genomic, proteomic, and other ?omic? data. HDEs involving whole-genome sequences and polymorphisms, expression levels of genes, protein abundance measurements, and combinations thereof have become a vanguard for new analytic approaches to the analysis of HDE data. Such situations demand creative approaches to the processes of statistical inference, estimation, prediction, classification, and study design. The novel and challenging biological questions asked from HDE data have resulted in many specialized analytic techniques being developed. This chapter discusses some of the unique statistical challenges facing investigators studying high-dimensional biology, and describes some approaches being developed by statistical scientists. We have included some focus on the increasing interest in questions involving testing multiple propositions simultaneously, appropriate inferential indicators for the types of questions biologists are interested in, and the need for replication of results across independent studies, investigators, and settings. A key consideration inherent throughout is the challenge in providing methods that a statistician judges to be sound and a biologist finds informative. äChallenges and Approaches to Statistical Design and Inference in High (epmc).pdf DeepDyve Pubget Overpricing