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Learning Clinical Outcomes from Heterogeneous Genomic Data Sources

Year: 2019

Type: preprint

Abstract: 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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1904.01637

Authors: Yousefi, Safoora, Shaban, Amirreza, Amgad, Mohamed, Chandradevan, Ramraj, Cooper, Lee A. D.

Institutions: Emory University, Georgia Institute of Technology, University of Moratuwa

Language: English

Topic: Gene expression and cancer classification

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Cancer Institute