首页|University of Oklahoma Reports Findings in Machine Learning (MetaPhenotype: A Tr ansferable Meta-Learning Model for Single- Cell Mass Spectrometry-Based Cell Phen otype Prediction Using Limited Number of Cells)
University of Oklahoma Reports Findings in Machine Learning (MetaPhenotype: A Tr ansferable Meta-Learning Model for Single- Cell Mass Spectrometry-Based Cell Phen otype Prediction Using Limited Number of Cells)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Machine Learning is th e subject of a report. According to newsoriginating from Norman, Oklahoma, by N ewsRx correspondents, research stated, “Single-cell mass spectrometry(SCMS) is an emerging tool for studying cell heterogeneity according to variation of molec ularspecies in single cells. Although it has become increasingly common to empl oy machine learning modelsin SCMS data analysis, such as the classification of cell phenotypes, the existing machine learning modelsoften suffer from low adap tability and transferability.”
NormanOklahomaUnited StatesNorth a nd Central AmericaCyborgsEmerging TechnologiesGeneticsMachine LearningMeta Learning
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