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XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data.

Eloise Withnell^1,2, Xiaoyu Zhang¹, Kai Sun¹

¹Data Science Institute Imperial College London, SW7 2AZ London, UK.

Briefings in Bioinformatics

|August 17, 2021

Summary

This summary is machine-generated.

Deep learning models in omics are often black boxes. We developed XOmiVAE, an interpretable deep learning model for cancer classification, to reveal gene contributions and discover new biological insights.

Keywords:

cancer classification deep learning explainable artificial intelligence gene expression omics data

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Deep learning models in omics research lack explainability, hindering trust and application.
The 'black box' nature of these models limits their practical use in biomedicine.

Purpose of the Study:

To introduce XOmiVAE, a novel interpretable deep learning model based on variational autoencoders (VAEs).
To enable explainable cancer classification and clustering using high-dimensional omics data.

Main Methods:

Developed XOmiVAE, a VAE-based interpretable deep learning architecture.
Applied XOmiVAE to high-dimensional omics data for cancer classification and clustering.
Analyzed gene contributions and latent dimension correlations within the model.

Main Results:

XOmiVAE reveals gene contributions to classification and correlations between genes and latent dimensions.
The model successfully explains both supervised classification and unsupervised clustering results.
Explainable results were validated against existing biomedical knowledge and downstream task performance.

Conclusions:

XOmiVAE enhances the credibility and practical implementation of deep learning in omics.
The model demonstrates potential for discovering novel biomedical knowledge from omics data.
XOmiVAE is a pioneering activation level-based interpretable deep learning model for VAE-generated clusters.