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10.1093/bioinformatics/btw710 http://scihub22266oqcxt.onion/10.1093/bioinformatics/btw710 C5860017!5860017 !27998936     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=27998936 &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       Bioinformatics 2017 ; 33 (6 ): 909-916 Nephropedia Template TP {{tp|p=27998936 |t=2017. CellSort: a support vector machine tool for optimizing fluorescence-activated cell sorting and reducing experimental effort |pdf=|usr=}}{{27998936 }} gab.com Text CellSort: a support vector machine tool for optimizing fluorescence-activated cell sorting and reducing experimental effort JO: Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=27998936 #FOAvip MOTIVATION: High throughput screening by fluorescence activated cell sorting (FACS) is a common task in protein engineering and directed evolution. It can also be a rate-limiting step if high false positive or negative rates necessitate multiple rounds of enrichment. Current FACS software requires the user to define sorting gates by intuition and is practically limited to two dimensions. In cases when multiple rounds of enrichment are required, the software cannot forecast the enrichment effort required. RESULTS: We have developed CellSort, a support vector machine (SVM) algorithm that identifies optimal sorting gates based on machine learning using positive and negative control populations. CellSort can take advantage of more than two dimensions to enhance the ability to distinguish between populations. We also present a Bayesian approach to predict the number of sorting rounds required to enrich a population from a given library size. This Bayesian approach allowed us to determine strategies for biasing the sorting gates in order to reduce the required number of enrichment rounds. This algorithm should be generally useful for improve sorting outcomes and reducing effort when using FACS. AVAILABILITY AND IMPLEMENTATION: Source code available at http://tyolab.northwestern.edu/tools/ . k-tyo@northwestern.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Twit Text FOAVip CellSort: a support vector machine tool for optimizing fluorescence-activated cell sorting and reducing experimental effort ????Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=27998936 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27998936 #FOAvip English Wikipedia <ref>{{Cite pmid|27998936 }}</ref> CellSort: a support vector machine tool for optimizing fluorescence-activated cell sorting and reducing experimental effort #MMPMID27998936 Yu JS ; Pertusi DA ; Adeniran AV ; Tyo KEJ Bioinformatics 2017[Mar]; 33 (6 ): 909-916 PMID27998936 show ga MOTIVATION: High throughput screening by fluorescence activated cell sorting (FACS) is a common task in protein engineering and directed evolution. It can also be a rate-limiting step if high false positive or negative rates necessitate multiple rounds of enrichment. Current FACS software requires the user to define sorting gates by intuition and is practically limited to two dimensions. In cases when multiple rounds of enrichment are required, the software cannot forecast the enrichment effort required. RESULTS: We have developed CellSort, a support vector machine (SVM) algorithm that identifies optimal sorting gates based on machine learning using positive and negative control populations. CellSort can take advantage of more than two dimensions to enhance the ability to distinguish between populations. We also present a Bayesian approach to predict the number of sorting rounds required to enrich a population from a given library size. This Bayesian approach allowed us to determine strategies for biasing the sorting gates in order to reduce the required number of enrichment rounds. This algorithm should be generally useful for improve sorting outcomes and reducing effort when using FACS. AVAILABILITY AND IMPLEMENTATION: Source code available at http://tyolab.northwestern.edu/tools/ . k-tyo@northwestern.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |*Software [MESH] |*Support Vector Machine [MESH] |Algorithms [MESH] |Bayes Theorem [MESH] |Cell Separation/*methods [MESH] |Flow Cytometry/*methods [MESH]CellSort: a support vector machine tool for optimizing fluorescence-ac(epmc).pdf DeepDyve Pubget Overpricing