ArticleNature communications2025
A mechanism-informed deep neural network enables prioritization of regulators that drive cell state transitions.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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.
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Who cites it
10 citing papers in PubMed.
- TF-GateNet: An Interpretable and Biologically Guided Framework for Primary-Metastatic State Prediction from Somatic Genomic Alterations.Biomolecules · 2026Article
- Cell lineage tracing: Methods, applications, and challenges.Quantitative biology (Beijing, China) · 2025Review
- Single-Cell Multi-Omics in Type 2 Diabetes Mellitus: Revealing Cellular Heterogeneity and Mechanistic Insights.International journal of molecular sciences · 2025Review
- A graph neural network model for inferring interindividual variation from experimental biological data.Scientific reports · 2025Article
- Deciphering hierarchical regulatory network of cell fate via an epigenetics-informed heterogeneous graph transformer on single-cell multi-omics data.Briefings in bioinformatics · 2025Article
- LogicSR: prior-guided symbolic regression for gene regulatory network inference from single-cell transcriptomics data.Briefings in bioinformatics · 2025Article
- HKDE-LACM: a hybrid model for lactic acid bacteria classification via k-mer and DNABERT-2 embedding fusion with cyclic DE-BO optimization.BMC genomics · 2025Article
- Phage-mediated horizontal transfer ofiMeta · 2025Article
- A mechanism-informed deep neural network enables prioritization of regulators that drive cell state transitions.Nature communications · 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cells are regulated at multiple levels, from regulations of individual genes to interactions across multiple genes. Some recent neural network models can connect molecular changes to cellular phenotypes, but their design lacks modeling of regulatory mechanisms, limiting the decoding of regulations behind key cellular events, such as cell state transitions. Here, we present regX, a deep neural network incorporating both gene-level regulation and gene-gene interaction mechanisms, which enables prioritizing potential driver regulators of cell state transitions and providing mechanistic interpretations. Applied to single-cell multi-omics data on type 2 diabetes and hair follicle development, regX reliably prioritizes key transcription factors and candidate cis-regulatory elements that drive cell state transitions. Some regulators reveal potential new therapeutic targets, drug repurposing possibilities, and putative causal single nucleotide polymorphisms. This method to analyze single-cell multi-omics data demonstrates how the interpretable design of neural networks can better decode biological systems.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.