ArticlePatterns (New York, N.Y.)2024
Latent space arithmetic on data embeddings from healthy multi-tissue human RNA-seq decodes disease modules.
Article in Patterns (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Leveraging neural network models for drug repurposing: a case study on cardiac hypertrophy.Scientific reports · 2026Article
- Decoding extremophiles: insights from bioinformatics, machine learning, and data-driven approaches.Briefings in bioinformatics · 2026Review
- Deep Learning-Enabled Multi-Omics Integration: A New Frontier in Precise Drug Target Discovery.Biology · 2026Review
- The human metabolome and machine learning improves predictions of the post-mortem interval.Nature communications · 2026Article
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Authors and funding
7 authors.
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Abstract
Computational analyses of transcriptomic data have dramatically improved our understanding of complex diseases. However, such approaches are limited by small sample sets of disease-affected material. We asked if a variational autoencoder trained on large groups of healthy human RNA sequencing (RNA-seq) data can capture the fundamental gene regulation system and generalize to unseen disease changes. Importantly, we found this model to successfully compress unseen transcriptomic changes from 25 independent disease datasets. We decoded disease-specific signals from the latent space and found them to contain more disease-specific genes than the corresponding differential expression analysis in 20 of 25 cases. Finally, we matched these disease signals with known drug targets and extracted sets of known and potential pharmaceutical candidates. In summary, our study demonstrates how data-driven representation learning enables the arithmetic deconstruction of the latent space, facilitating the dissection of disease mechanisms and drug targets.
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