ArticleGenome biology2025
Cell Decoder: decoding cell identity with multi-scale explainable deep learning.
Article in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- scProtoTransformer: Scalable reference mapping across molecules, cells, and donors.Science advances · 2026Article
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- Exploring the prognostic role of senescence-related genes in gastric cancer through multi-omics integration and machine learning.Human genomics · 2026Article
- Cell Decoder: decoding cell identity with multi-scale explainable deep learning.Genome biology · 2025Article
- Pathway-guided architectures for interpretable AI in biological research.Computational and structural biotechnology journal · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
backgroundCells are the fundamental units of life, and understanding their diversity and functionality requires detailed characterization. The rise of single-cell omics data enables this, yet current deep learning approaches lack multi-scale interpretability.
resultsWe introduce Cell Decoder, a model that integrates biological prior knowledge to provide a multi-scale representation of cells. Using automated machine learning and post hoc analysis, Cell Decoder decodes cell identity and outperforms existing methods. It offers multi-view interpretability and facilitates data integration.
conclusionsApplied to human bone and mouse embryonic data, Cell Decoder reveals the multi-scale heterogeneity of cell identities, providing a powerful framework for advancing our understanding of cellular diversity.
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Registered trials
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.