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- W3008659867 abstract "Abstract Achieving precision oncology requires accurate identification of targetable cancer vulnerabilities in patients. Generally, genomic features are regarded as the state-of-the-art method for stratifying patients for targeted therapies. In this work, we conduct the first rigorous comparison of DNA- and expression-based predictive models for viability across five datasets encompassing chemical and genetic perturbations. We find that expression consistently outperforms DNA for predicting vulnerabilities, including many currently stratified by canonical DNA markers. Contrary to their perception in the literature, the most accurate expression-based models depend on few features and are amenable to biological interpretation. This work points to the importance of exploring more comprehensive expression profiling in clinical settings." @default.
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- W3008659867 date "2020-02-24" @default.
- W3008659867 modified "2023-10-16" @default.
- W3008659867 title "Gene expression has more power for predicting <i>in vitro</i> cancer cell vulnerabilities than genomics" @default.
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- W3008659867 doi "https://doi.org/10.1101/2020.02.21.959627" @default.
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