ArticleNature cell biology2025
CellNavi predicts genes directing cellular transitions by learning a gene graph-enhanced cell state manifold.
Article in Nature cell biology, 2025. 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.
- Artificial intelligence virtual bone organoids (AIVBOs).Journal of orthopaedic translation · 2026Review
- Synthetic developmental engineering of human liver organogenesis.Development (Cambridge, England) · 2026Review
- ExIR enables prioritizing driver and biomarker genes from omics data in a reference free manner.iScience · 2026Article
- Toward trustworthy virtual cells: a roadmap for perturbation-resolved, context-aware, and experimentally validated cell models.Frontiers in cell and developmental biology · 2026Review
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Authors and funding
12 authors.
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Abstract
A select few genes act as pivotal drivers in the process of cell state transitions. However, finding key genes involved in different transitions is challenging. Here, to address this problem, we present CellNavi, a deep learning-based framework designed to predict genes that drive cell state transitions. CellNavi builds a driver gene predictor upon a cell state manifold, which captures the intrinsic features of cells by learning from large-scale, high-dimensional transcriptomics data and integrating gene graphs with directional connections. Our analysis shows that CellNavi can accurately predict driver genes for transitions induced by genetic, chemical and cytokine perturbations across diverse cell types, conditions and studies. By leveraging a biologically meaningful cell state manifold, it is proficient in tasks involving critical transitions such as cellular differentiation, disease progression and drug response. CellNavi represents a substantial advancement in driver gene prediction and cell state manipulation, opening new avenues in disease biology and therapeutic discovery.
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