Learning Cancer Outcomes from Heterogeneous Genomic Data Sources: An Adversarial Multi-Task Learning Approach
Abstract
Translating the high-dimensional 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. 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. Experiments demonstrate that the proposed method helps mitigate data scarcity and outcome censorship in cancer genomics learning problems.
Cite
Text
Yousefi et al. "Learning Cancer Outcomes from Heterogeneous Genomic Data Sources: An Adversarial Multi-Task Learning Approach." ICML 2019 Workshops: AMTL, 2019.Markdown
[Yousefi et al. "Learning Cancer Outcomes from Heterogeneous Genomic Data Sources: An Adversarial Multi-Task Learning Approach." ICML 2019 Workshops: AMTL, 2019.](https://mlanthology.org/icmlw/2019/yousefi2019icmlw-learning/)BibTeX
@inproceedings{yousefi2019icmlw-learning,
title = {{Learning Cancer Outcomes from Heterogeneous Genomic Data Sources: An Adversarial Multi-Task Learning Approach}},
author = {Yousefi, Safoora and Shaban, Amirreza and Amgad, Mohamed and Cooper, Lee},
booktitle = {ICML 2019 Workshops: AMTL},
year = {2019},
url = {https://mlanthology.org/icmlw/2019/yousefi2019icmlw-learning/}
}