ArticleNature communications2020
Deriving disease modules from the compressed transcriptional space embedded in a deep autoencoder.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Leveraging neural network models for drug repurposing: a case study on cardiac hypertrophy.Scientific reports · 2026Article
- AI and network biology for rational polypharmacology in signaling drug design: a review.NPJ precision oncology · 2026Review
- Decoding extremophiles: insights from bioinformatics, machine learning, and data-driven approaches.Briefings in bioinformatics · 2026Review
- Infusing structural assumptions into dimensionality reduction for single-cell RNA sequencing data to identify small gene sets.Communications biology · 2025Article
- Towards the Next Generation of Data-Driven Therapeutics Using Spatially Resolved Single-Cell Technologies and Generative AI.European journal of immunology · 2025Review
- Latent space arithmetic on data embeddings from healthy multi-tissue human RNA-seq decodes disease modules.Patterns (New York, N.Y.) · 2024Article
- CGMega: explainable graph neural network framework with attention mechanisms for cancer gene module dissection.Nature communications · 2024Article
- Structure-primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inference.Genome biology · 2024Article
- Precise and interpretable neural networks reveal epigenetic signatures of aging across youth in health and disease.Frontiers in aging · 2024Article
- XA4C: eXplainable representation learning via Autoencoders revealing Critical genes.PLoS computational biology · 2023Article
- NCAE: data-driven representations using a deep network-coherent DNA methylation autoencoder identify robust disease and risk factor signatures.Briefings in bioinformatics · 2023Article
- Predicting molecular mechanisms of hereditary diseases by using their tissue-selective manifestation.Molecular systems biology · 2023Article
- Biologically informed variational autoencoders allow predictive modeling of genetic and drug-induced perturbations.Bioinformatics (Oxford, England) · 2023Article
- Machine learning in rare disease.Nature methods · 2023Review
- Association between prenatal alcohol exposure and children's facial shape: a prospective population-based cohort study.Human reproduction (Oxford, England) · 2023Article
- Identifying prognostic subgroups of luminal-A breast cancer using deep autoencoders and gene expressions.PLoS computational biology · 2023Article
- siVAE: interpretable deep generative models for single-cell transcriptomes.Genome biology · 2023Article
- Animal models and animal-free innovations for cardiovascular research: current status and routes to be explored. Consensus document of the ESC Working Group on Myocardial Function and the ESC Working Group on Cellular Biology of the Heart.Cardiovascular research · 2022Article
- SD2: spatially resolved transcriptomics deconvolution through integration of dropout and spatial information.Bioinformatics (Oxford, England) · 2022Article
- An interolog-based barley interactome as an integration framework for immune signaling.Genetics · 2022Article
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4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Disease modules in molecular interaction maps have been useful for characterizing diseases. Yet biological networks, that commonly define such modules are incomplete and biased toward some well-studied disease genes. Here we ask whether disease-relevant modules of genes can be discovered without prior knowledge of a biological network, instead training a deep autoencoder from large transcriptional data. We hypothesize that modules could be discovered within the autoencoder representations. We find a statistically significant enrichment of genome-wide association studies (GWAS) relevant genes in the last layer, and to a successively lesser degree in the middle and first layers respectively. In contrast, we find an opposite gradient where a modular protein-protein interaction signal is strongest in the first layer, but then vanishing smoothly deeper in the network. We conclude that a data-driven discovery approach is sufficient to discover groups of disease-related genes.
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