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Combining Supervised and Unsupervised Machine Learning Methods for Phenotypic Functional Genomics Screening.

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第一作者： Wienand A,Omta

第一单位： Department of Cell Biology, Centre for Molecular Medicine, UMC Utrecht, Utrecht, The Netherlands.;Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands.;Core Life Analytics B.V., Utrecht, The Netherlands.

作者： Wienand A,Omta ^[1] ; Roy G,van Heesbeen ^[2] ; Ian,Shen ^[3] ; Jacob,de Nobel ^[4] ; Desmond,Robers ^[4] ; Lieke M,van der Velden ^[4] ; René H,Medema ^[2] ; Arno P J M,Siebes ^[3] ; Ad J,Feelders ^[3] ; Sjaak,Brinkkemper ^[3] ; Judith S,Klumperman ^[4] ; Marco René,Spruit ^[3] ; Matthieu J S,Brinkhuis ^[3] ; David A,Egan ^[5]

作者单位： Department of Cell Biology, Centre for Molecular Medicine, UMC Utrecht, Utrecht, The Netherlands.;Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands.;Core Life Analytics B.V., Utrecht, The Netherlands. ^[1] Department of Cell Biology, NKI-AVL, Amsterdam, Noord-Holland, The Netherlands. ^[2] Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands. ^[3] Department of Cell Biology, Centre for Molecular Medicine, UMC Utrecht, Utrecht, The Netherlands. ^[4] Core Life Analytics B.V., Utrecht, The Netherlands. ^[5]

关键词 artificial intelligence classification phenotypic profiles supervised machine learning

医学主题词基因组, 人(Genome, Human);基因组学(Genomics);高通量筛选分析(High-Throughput Screening Assays);人类(Humans);表型(Phenotype);RNA, 小分子干扰(RNA, Small Interfering)

DOI 10.1177/2472555220919345

PMID 32400262

发布时间 2022-08-30

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准确一般字义偏差语意偏离译文作用小提交