ReviewNature reviews. Genetics2021
Statistical mechanics meets single-cell biology.
Review in Nature reviews. Genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 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
70 citing papers in PubMed.
- Latent causal diffusions for single-cell perturbation modeling.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Tissue-Level Transcriptomic Entropy Reveals Organ-Specific Aging Patterns and Predicts Cancer Progression.Aging cell · 2026Article
- Embracing non-linearity in human ageing.Nature reviews. Genetics · 2026Review
- Sensitivity analysis of cell fate trajectories from single-cell transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- FIERCE: reconstructing dynamic trajectories from the differentiation potency of single cells.Bioinformatics (Oxford, England) · 2026Article
- Article
- Bayesian inference of haematopoietic stem/progenitor cell differentiation phenotypic manifolds and their bifurcation points using Gaussian processes and Gibbs sampling.Scientific reports · 2026Article
- How Does Genetic Information Enable Life?Bulletin of mathematical biology · 2026Article
- Unveiling gene perturbation effects through gene regulatory networks inference from single-cell transcriptomic data.PLoS computational biology · 2026Article
- Generative models of cell dynamics: from Neural ODEs to flow matching.Communications biology · 2026Review
- Parameter-free representations outperform single-cell foundation models on downstream benchmarks.ArXiv · 2026Article
- Parameter-free representations outperform single-cell foundation models on downstream benchmarks.bioRxiv : the preprint server for biology · 2026Article
- DeepCE: a deep learning framework for correlation-enhanced gene regulatory network inference in single-cell RNA sequencing data.Bioinformatics advances · 2026Article
- Detection of pre-transition phases during biological development using single-sample network entropy (SNE).NPJ systems biology and applications · 2025Article
- The intrinsic dimension of gene expression during cell differentiation.Nucleic acids research · 2025Article
- Dynamical systems theory as an organizing principle for single-cell biology.NPJ systems biology and applications · 2025Review
- Escaping ageing through Cell Annealing-a phenomenological model.Cell research · 2025Article
- Adapting systems biology to address the complexity of human disease in the single-cell era.Nature reviews. Genetics · 2025Review
- Dynamical systems of fate and form in development.Seminars in cell & developmental biology · 2025Review
- Modeling combinatorial regulation from single-cell multi-omics provides regulatory units underpinning cell type landscape using cRegulon.Genome biology · 2025Article
10 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Single-cell omics is transforming our understanding of cell biology and disease, yet the systems-level analysis and interpretation of single-cell data faces many challenges. In this Perspective, we describe the impact that fundamental concepts from statistical mechanics, notably entropy, stochastic processes and critical phenomena, are having on single-cell data analysis. We further advocate the need for more bottom-up modelling of single-cell data and to embrace a statistical mechanics analysis paradigm to help attain a deeper understanding of single-cell systems biology.
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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.