Detecting cancer dependencies is key to disease treatment. Recent efforts have mapped gene dependencies and drug sensitivities in hundreds of cancer cell lines. These data allow us to learn for the first time models of tumor vulnerabilities and apply them to suggest novel drug targets. Here we devise novel deep learning methods for predicting gene dependencies and drug sensitivities from gene expression measurements. By combining dimensionality reduction strategies, we are able to learn accurate models that outperform simpler neural networks or linear models.

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使用深度学习从表达数据预测癌症依赖性

检测癌症依赖性是疾病治疗的关键。最近的努力已经绘制了数百种癌细胞系中的基因依赖性和药物敏感性。这些数据使我们能够首次了解肿瘤脆弱性的模型并将其应用于建议新的药物靶点。在这里，我们设计了新的深度学习方法，用于从基因表达测量中预测基因依赖性和药物敏感性。通过结合降维策略，我们能够学习出优于简单神经网络或线性模型的准确模型。