论文标题
稳定驱动的药物反应解释预测方案（Stadrip）
A stability-driven protocol for drug response interpretable prediction (staDRIP)
论文作者
论文摘要
现代癌症 - 组和药理数据在精确的癌症医学方面具有巨大的希望，可用于开发个性化患者治疗。但是，此类数据中的高异质性和噪声对预测癌细胞对治疗药物的反应的反应构成了挑战。结果，在整个预测建模管道中，任意人类的判断呼叫猖ramp。在这项工作中，我们开发了一种透明的稳定性驱动的管道，用于药物反应可解释的预测或Stadrip，该管道建立在PCS框架的垂直数据科学框架（Yu and Kumbier，2020年）上，并减轻人类判断的影响。在这里，我们在癌症研究中首次使用PCS框架来提取在预测药物反应和跨适当数据和模型扰动中稳定的蛋白质和基因。在使用癌细胞系百科全书（CCLE）数据的24种最稳定蛋白质中，有18个与药物反应相关，或在先前文献中被鉴定为已知或可能的药物靶标，这证明了我们稳定性驱动的管道对癌症药物反应预测模型中知识发现的知识发现的实用性。
Modern cancer -omics and pharmacological data hold great promise in precision cancer medicine for developing individualized patient treatments. However, high heterogeneity and noise in such data pose challenges for predicting the response of cancer cell lines to therapeutic drugs accurately. As a result, arbitrary human judgment calls are rampant throughout the predictive modeling pipeline. In this work, we develop a transparent stability-driven pipeline for drug response interpretable predictions, or staDRIP, which builds upon the PCS framework for veridical data science (Yu and Kumbier, 2020) and mitigates the impact of human judgment calls. Here we use the PCS framework for the first time in cancer research to extract proteins and genes that are important in predicting the drug responses and stable across appropriate data and model perturbations. Out of the 24 most stable proteins we identified using data from the Cancer Cell Line Encyclopedia (CCLE), 18 have been associated with the drug response or identified as a known or possible drug target in previous literature, demonstrating the utility of our stability-driven pipeline for knowledge discovery in cancer drug response prediction modeling.
