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10.1038/s41598-017-11665-4 http://scihub22266oqcxt.onion/10.1038/s41598-017-11665-4 C5595802!5595802 !28900181     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 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\28900181 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Sci+Rep 2017 ; 7 (1 ): 11347 Nephropedia Template TP {{tp|p=28900181 |t=2017. Heterogeneity Aware Random Forest for Drug Sensitivity Prediction |pdf=|usr=}}{{28900181 }} gab.com Text Heterogeneity Aware Random Forest for Drug Sensitivity Prediction JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=28900181 #FOAvip Samples collected in pharmacogenomics databases typically belong to various cancer types. For designing a drug sensitivity predictive model from such a database, a natural question arises whether a model trained on diverse inter-tumor heterogeneous samples will perform similar to a predictive model that takes into consideration the heterogeneity of the samples in model training and prediction. We explore this hypothesis and observe that ensemble model predictions obtained when cancer type is known out-perform predictions when that information is withheld even when the samples sizes for the former is considerably lower than the combined sample size. To incorporate the heterogeneity idea in the commonly used ensemble based predictive model of Random Forests, we propose Heterogeneity Aware Random Forests (HARF) that assigns weights to the trees based on the category of the sample. We treat heterogeneity as a latent class allocation problem and present a covariate free class allocation approach based on the distribution of leaf nodes of the model ensemble. Applications on CCLE and GDSC databases show that HARF outperforms traditional Random Forest when the average drug responses of cancer types are different. Twit Text FOAVip Heterogeneity Aware Random Forest for Drug Sensitivity Prediction ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=28900181 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28900181 #FOAvip English Wikipedia <ref>{{Cite pmid|28900181 }}</ref> Heterogeneity Aware Random Forest for Drug Sensitivity Prediction #MMPMID28900181 Rahman R ; Matlock K ; Ghosh S ; Pal R Sci Rep 2017[Sep]; 7 (1 ): 11347 PMID28900181 show ga Samples collected in pharmacogenomics databases typically belong to various cancer types. For designing a drug sensitivity predictive model from such a database, a natural question arises whether a model trained on diverse inter-tumor heterogeneous samples will perform similar to a predictive model that takes into consideration the heterogeneity of the samples in model training and prediction. We explore this hypothesis and observe that ensemble model predictions obtained when cancer type is known out-perform predictions when that information is withheld even when the samples sizes for the former is considerably lower than the combined sample size. To incorporate the heterogeneity idea in the commonly used ensemble based predictive model of Random Forests, we propose Heterogeneity Aware Random Forests (HARF) that assigns weights to the trees based on the category of the sample. We treat heterogeneity as a latent class allocation problem and present a covariate free class allocation approach based on the distribution of leaf nodes of the model ensemble. Applications on CCLE and GDSC databases show that HARF outperforms traditional Random Forest when the average drug responses of cancer types are different. |*Drug Resistance [MESH] |*Models, Statistical [MESH] |Algorithms [MESH] |Databases, Factual [MESH] |Humans [MESH] |Neoplasms/drug therapy [MESH]Heterogeneity Aware Random Forest for Drug Sensitivity Prediction (epmc).pdf DeepDyve Pubget Overpricing