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- W4289884158 abstract "Machine learning methods trained on cancer cell line panels are intensively studied for the prediction of optimal anti-cancer therapies. While classification approaches distinguish effective from ineffective drugs, regression approaches aim to quantify the degree of drug effectiveness. However, the high specificity of most anti-cancer drugs induces a skewed distribution of drug response values in favor of the more drug-resistant cell lines, negatively affecting the classification performance (class imbalance) and regression performance (regression imbalance) for the sensitive cell lines. Here, we present a novel approach called SimultAneoUs Regression and classificatiON Random Forests (SAURON-RF) based on the idea of performing a joint regression and classification analysis. We demonstrate that SAURON-RF improves the classification and regression performance for the sensitive cell lines at the expense of a moderate loss for the resistant ones. Furthermore, our results show that simultaneous classification and regression can be superior to regression or classification alone." @default.
- W4289884158 created "2022-08-05" @default.
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- W4289884158 date "2022-08-05" @default.
- W4289884158 modified "2023-09-26" @default.
- W4289884158 title "Simultaneous regression and classification for drug sensitivity prediction using an advanced random forest method" @default.
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- W4289884158 doi "https://doi.org/10.1038/s41598-022-17609-x" @default.
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