ArticleBriefings in bioinformatics2021
XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 2 of them syntheses that pooled it.
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Who cites it
39 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep representation learning for temporal inference in cancer omics: a systematic literature review.Briefings in bioinformatics · 2026Pooled it
- A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data.BMC bioinformatics · 2023Pooled it
- TMO-Net+: An Enhanced Tumor Multi-Omics Pre-Trained Network for Multi-Task Learning in Oncology.Genes · 2026Article
- Article
- Speeding Up the Discovery of Optimal Feature Combinations for Omics Data Based on Pseudo-Kernel Function.Research square · 2026Article
- Article
- MOTCS: A Cancer Subtype Classification and Key Biomarker Recognition Model Based on Multi-Omics Data Integration of Transformer.International journal of molecular sciences · 2026Article
- Deep Learning-Enabled Multi-Omics Integration: A New Frontier in Precise Drug Target Discovery.Biology · 2026Review
- Predicting Postoperative Survival in Patients With Malignant Biliary Obstruction Using an Interpretable Machine Learning Model: A Multicenter Study.Cancer medicine · 2026Article
- Artificial intelligence-driven drug discovery: a deep learning paradigm shift in pharmaceutical research and development.Frontiers in pharmacology · 2026Review
- Complementary structure of statistical significance and predictive relevance in explainable machine learning-based transcriptomic tissue classification of Hanwoo cattle.Frontiers in genetics · 2026Article
- Recent advances in omics-based research of mitral valve disease.Frontiers in cardiovascular medicine · 2026Review
- Multi-omics profiling and AI-driven clinically deployable risk models in MGUS and smoldering myeloma.Clinical and experimental medicine · 2025Review
- E-ABIN: an explainable module for anomaly detection in biological networks.NAR genomics and bioinformatics · 2025Article
- Interpretable Multi-Cancer Early Detection Using SHAP-Based Machine Learning on Tumor-Educated Platelet RNA.Diagnostics (Basel, Switzerland) · 2025Article
- Deep Learning Applications in Clinical Cancer Detection: A Review of Implementation Challenges and Solutions.Mayo Clinic proceedings. Digital health · 2025Review
- Novel cancer subtyping method guided by tumor-normal sample in latent space of transcriptomic variational autoencoder.Scientific reports · 2025Article
- A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches.Briefings in bioinformatics · 2025Review
- Multimodal CustOmics: A unified and interpretable multi-task deep learning framework for multimodal integrative data analysis in oncology.PLoS computational biology · 2025Article
- MLOmics: Cancer Multi-Omics Database for Machine Learning.Scientific data · 2025Article
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4 authors.
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Abstract
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 also 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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.