标题： 从异构基因组数据源中学习临床结果 显示英文标题

标题： Learning Clinical Outcomes from Heterogeneous Genomic Data Sources

摘要： 将基因组平台产生的大量数据转化为可靠的临床结果预测仍然是实现基因组医学潜力中的一个关键挑战，这主要是由于独立样本数量较少。 在本文中，我们表明可以通过多任务学习和对抗性表示学习，利用异构的基因组数据源训练神经网络来预测临床结果，从而使多个队列和结果能够在训练中结合。 我们将提出的方法与两种基线方法进行比较，并证明它可以用于帮助缓解癌症基因组学学习问题中的数据稀缺性和临床结果截断问题。 显示英文摘要

摘要： Translating the vast data generated by genomic platforms into reliable predictions of clinical outcomes remains a critical challenge in realizing the promise of genomic medicine largely due to small number of independent samples. In this paper, we show that neural networks can be trained to predict clinical outcomes using heterogeneous genomic data sources via multi-task learning and adversarial representation learning, allowing one to combine multiple cohorts and outcomes in training. We compare our proposed method to two baselines and demonstrate that it can be used to help mitigate the data scarcity and clinical outcome censorship in cancer genomics learning problems.