November 2024 · Nature Communications · First author

Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning

Zhaoxiang Cai, Samuel Apolinário, Ana R Baião, Clare Pacini, Miguel D Sousa, Susana Vinga, Emanuel Gonçalves

Abstract

We introduce MOSA (Multi-Omic Synthetic Augmentation), an unsupervised deep learning model for integrating and augmenting cancer multi-omics data. By leveraging variational autoencoders, MOSA generates synthetic multi-omic profiles that expand the effective sample size of cancer datasets, enabling the discovery of new biomarkers and drug targets. We demonstrate that MOSA-augmented data improves the power of association studies and clustering analyses, providing a valuable resource for the cancer research community.

BibTeX

@article{cai2024cai2024mosa,
  title = {{Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning}},
  author = {Zhaoxiang Cai and Samuel Apolinário and Ana R Baião and Clare Pacini and Miguel D Sousa and Susana Vinga and Emanuel Gonçalves},
  journal = {Nature Communications},
  year = {2024},
  doi = {10.1038/s41467-024-54771-4}
}