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10.1093/bioinformatics/bty257 http://scihub22266oqcxt.onion/10.1093/bioinformatics/bty257 C6022689!6022689!29949984     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Bioinformatics 2018 ; 34 (13): i395-403 Nephropedia Template TP {{tp|p=29949984|t=2018. Improving genomics-based predictions for precision medicine through active elicitation of expert knowledge |pdf=|usr=}}{{29949984}} gab.com Text Improving genomics-based predictions for precision medicine through active elicitation of expert knowledge JO: Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=29949984 #FOAvip Motivation: Precision medicine requires the ability to predict the efficacies of different treatments for a given individual using high-dimensional genomic measurements. However, identifying predictive features remains a challenge when the sample size is small. Incorporating expert knowledge offers a promising approach to improve predictions, but collecting such knowledge is laborious if the number of candidate features is very large. Results: We introduce a probabilistic framework to incorporate expert feedback about the impact of genomic measurements on the outcome of interest and present a novel approach to collect the feedback efficiently, based on Bayesian experimental design. The new approach outperformed other recent alternatives in two medical applications: prediction of metabolic traits and prediction of sensitivity of cancer cells to different drugs, both using genomic features as predictors. Furthermore, the intelligent approach to collect feedback reduced the workload of the expert to approximately 11%, compared to a baseline approach. Availability and implementation: Source code implementing the introduced computational methods is freely available at https://github.com/AaltoPML/knowledge-elicitation-for-precision-medicine. Supplementary information: Supplementary data are available at Bioinformatics online. Twit Text FOAVip Improving genomics-based predictions for precision medicine through active elicitation of expert knowledge ????Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=29949984 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29949984 #FOAvip English Wikipedia <ref>{{Cite pmid|29949984}}</ref> Improving genomics-based predictions for precision medicine through active elicitation of expert knowledge #MMPMID29949984 Sundin I; Peltola T; Micallef L; Afrabandpey H; Soare M; Mamun Majumder M; Daee P; He C; Serim B; Havulinna A; Heckman C; Jacucci G; Marttinen P; Kaski S Bioinformatics 2018[Jul]; 34 (13): i395-403 PMID29949984show ga Motivation: Precision medicine requires the ability to predict the efficacies of different treatments for a given individual using high-dimensional genomic measurements. However, identifying predictive features remains a challenge when the sample size is small. Incorporating expert knowledge offers a promising approach to improve predictions, but collecting such knowledge is laborious if the number of candidate features is very large. Results: We introduce a probabilistic framework to incorporate expert feedback about the impact of genomic measurements on the outcome of interest and present a novel approach to collect the feedback efficiently, based on Bayesian experimental design. The new approach outperformed other recent alternatives in two medical applications: prediction of metabolic traits and prediction of sensitivity of cancer cells to different drugs, both using genomic features as predictors. Furthermore, the intelligent approach to collect feedback reduced the workload of the expert to approximately 11%, compared to a baseline approach. Availability and implementation: Source code implementing the introduced computational methods is freely available at https://github.com/AaltoPML/knowledge-elicitation-for-precision-medicine. Supplementary information: Supplementary data are available at Bioinformatics online. äImproving genomics-based predictions for precision medicine through ac(epmc).pdf DeepDyve Pubget Overpricing