首页|Lunenfeld-Tanenbaum Research Institute Reports Findings in Machine Learning (Bey ond benchmarking and towards predictive models of dataset-specific single-cell R NA-seq pipeline performance)
Lunenfeld-Tanenbaum Research Institute Reports Findings in Machine Learning (Bey ond benchmarking and towards predictive models of dataset-specific single-cell R NA-seq pipeline performance)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating in Toronto, Canad a, by NewsRx journalists, research stated, “The advent of singlecell RNA-sequen cing (scRNA-seq) has driven significant computational methods development for al l steps in the scRNA-seq data analysis pipeline, including filtering, normalizat ion, and clustering. The large number of methods and their resulting parameter c ombinations has created a combinatorial set of possible pipelines to analyze scR NA-seq data, which leads to the obvious question: which is best? Several benchma rking studies compare methods but frequently find variable performance depending on dataset and pipeline characteristics.”
TorontoCanadaNorth and Central Ameri caCyborgsEmerging TechnologiesGeneticsMachine Learning
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