In precision medicine, a common problem is drug sensitivity prediction from cancer tissue cell lines. These types of problems entail modelling multivariate drug responses on high-dimensional molecular feature sets in typically >1000 cell lines. The dimensions of the problem require specialised models and estimation methods. In addition, external information on both the drugs and the features is often available. We propose to model the drug responses through a linear regression with shrinkage enforced through a normal inverse Gaussian prior. We let the prior depend on the external information, and estimate the model and external information dependence in an empirical-variational Bayes framework. We demonstrate the usefulness of this model in both a simulated setting and in the publicly available Genomics of Drug Sensitivity in Cancer data.

中文翻译：

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由外部数据提供的具有正态逆高斯收缩的药物敏感性预测

在精准医疗中，一个常见的问题是来自癌症组织细胞系的药物敏感性预测。这些类型的问题需要对通常 > 1000 个细胞系中的高维分子特征集的多变量药物反应进行建模。问题的维度需要专门的模型和估计方法。此外，通常可以获得有关药物和特征的外部信息。我们建议通过线性回归对药物反应进行建模，并通过正常的逆高斯先验强制收缩。我们让先验依赖于外部信息，并在经验变分贝叶斯框架中估计模型和外部信息依赖。我们证明了该模型在模拟环境和公开可用的癌症药物敏感性基因组学数据中的有用性。