Deep learning based approaches have proven promising to model omicsdata. However, one of the current limitations compared to statistical and traditional machine learning approaches is the lack of explainability, which not only reduces the reliability, but limits the potential for acquiring novel knowledge from unpicking the " black-box "  models. Here we present XOmiVAE, a novel interpretable deep learning model for cancer classification using high-dimensional omics data. XOmiVAE is able to obtain contribution values of each gene and latent dimension for a specific prediction, and the correlation between genes and the latent dimensions . It is also revealed that XOmiVAE can explain both the supervised classification and the unsupervised clustering results from the deep learning network. To the best of our knowledge, XOmiVAE is one of the first activated-based deep learning interpretation method to explain novel clusters generated by variational autoencoders. 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 .
The lack of explainability is one of the most prominent disadvantages of deep learning applications in omics. This " black box " problem can undermine the credibility and limit the practical implementation of biomedical deep learning models. Here we present XOmiVAE, a variational autoencoder (VAE) based interpretable deep learning model for cancer classification using high-dimensional omics data. XOmiVAE is capable of revealing the contribution of each gene and latent dimension for each classification prediction, and the correlation between each gene and each latent dimension . It is also demonstrated that XOmiVAE can explain not only the supervised classification but the unsupervised clustering results from the deep learning network. To the best of our knowledge, XOmiVAE is one of the first activation level-based interpretable deep learning models explaining novel clusters generated by VAE. The explainable results generated by XOmiVAE were validated by both the performance of downstream tasks and the biomedical knowledge. In our experiments, XOmiVAE explanations of deep learning based cancer classification and clustering aligned with current domain knowledge including biological annotation and academic literature, which shows great potential for novel biomedical knowledge discovery from deep learning models .
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Deep learning based approaches have proven promising to model omicsdata. However, one of the current limitations compared to statistical and traditional machine learning approaches is the lack of explainability, which not only reduces the reliability, but limits the potential for acquiring novel knowledge from unpicking the
The lack of explainability is one of the most prominent disadvantages of deep learning applications in omics. This
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black-box
black box
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problem can undermine the credibility and limit the practical implementation of biomedical deep learning
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novel
variational autoencoder (VAE) based
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able to obtain contribution values
capable of revealing the contribution
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a specific
each classification
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genes and the latent dimensions
each gene and each latent dimension
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revealed
demonstrated
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both
not only
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and
but
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activated-based deep learning interpretation method to explain
activation level-based interpretable deep learning models explaining
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variational autoencoders. The
VAE. The explainable
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biomedical knowledge and the
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.
and the biomedical knowledge. In our experiments,
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academic
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. 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