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

Learning Clinical Outcomes from Heterogeneous Genomic Data Sources