Before revision:  The results generated by XOmiVAE were validated by both the biomedical knowledge and the performance of downstream tasks . XOmiVAE explanations of deep learning based cancer classification and clustering aligned with current domain knowledge including biological annotation and  literature, which shows great potential for novel biomedical knowledge discovery from deep learning models . The top XOmiVAE selected genes and dimensions shown significant influence to the performance of cancer classification. Additionally, we offer important steps to consider when interpreting deep learning models for tumour classification. For instance, we demonstrate the importance of choosing background samples that makes biological sense and the limitations of connection weight based methods to explain latent dimensions .
After revision:  The results generated by XOmiVAE were validated by both the biomedical knowledge and the performance of downstream tasks . XOmiVAE explanations of deep learning based cancer classification and clustering aligned with current domain knowledge including biological annotation and literature, which shows great potential for novel biomedical knowledge discovery from deep learning models .