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TUGDA: Task uncertainty guided domain adaptation for robust generalization of cancer drug response prediction from in vitro to in vivo settings

2020-12-18

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Over the last decade, large-scale cancer omics studies have highlighted the diversity of patient molecular profiles and the importance of leveraging this information to deliver the right drug to the right patient at the right time. Key challenges in learning predictive models for this include the high-dimensionality of omics data, limitations in the number of data points available, and heterogeneity in biological...

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Literature Corpus work: 18fcf8b2-c26c-503b-9451-0ec85e8b9f6f
DOI: 10.1101/2020.12.17.415737

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TUGDA: Task uncertainty guided domain adaptation for robust generalization of cancer drug response prediction from in vitro to in vivo settingsDOI 10.1101/2020.12.17.415737Read

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TUGDA: Task uncertainty guided domain adaptation for robust generalization of cancer drug response prediction from <i>in vitro</i> to <i>in vivo</i> settings

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