ReviewNature genetics2025
Causal machine learning for single-cell genomics.
Review in Nature genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 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
29 citing papers in PubMed.
- From Cluster to Claim: Calibrating Interpretation in Single-Cell Transcriptomics.Advanced genetics (Hoboken, N.J.) · 2026Article
- scProtoTransformer: Scalable reference mapping across molecules, cells, and donors.Science advances · 2026Article
- Single-Cell and Machine Learning Analyses Identify a PFKFB3-Centered Regulatory Network and Potential Salidroside Interaction in Coronary Heart Disease.International journal of molecular sciences · 2026Article
- Mathematical and Computational Models of Biochemical Reactions and Cell Signaling-From Ordinary Differential Equations to Machine Learning.International journal of molecular sciences · 2026Review
- Context-aware sequence-to-function model of human gene regulation.Nature communications · 2026Article
- Foundation models in omics research: a comprehensive survey.Briefings in bioinformatics · 2026Review
- Decoding and steering spatially resolved cellular dynamics.Molecular systems biology · 2026Article
- A causal reinforcement learning framework for reliable gene regulatory network inference.BMC bioinformatics · 2026Article
- Causal effect estimation from trans-regulatory single-cell CRISPR screens.Cell genomics · 2026Review
- Single-cell sequencing and machine learning-based prediction of spliceosome-associated factor 2 may represent potential targets for osteoarthritis.Osteoarthritis and cartilage open · 2026Article
- Gene regulatory networks: from correlative models to causal explanations.Nature reviews. Genetics · 2026Review
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- pertTF: context-aware AI modeling for genome-scale and cross-system perturbation prediction.bioRxiv : the preprint server for biology · 2026Article
- Generative models of cell dynamics: from Neural ODEs to flow matching.Communications biology · 2026Review
- A genome-scale single-cell CRISPRi map of trans gene regulation across human pluripotent stem cell lines.Cell genomics · 2026Article
- Single-cell disentangled representations for perturbation modeling and treatment effect estimation.bioRxiv : the preprint server for biology · 2026Article
- Article
- An explainable machine learning model predicts 30-day readmission after vertebral augmentation.iScience · 2026Article
- Article
- Temporal network analysis in systems biology: concepts, inference, and validation.Frontiers in bioinformatics · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advances in single-cell '-omics' allow unprecedented insights into the transcriptional profiles of individual cells and, when combined with large-scale perturbation screens, enable measuring of the effect of targeted perturbations on the whole transcriptome. These advances provide an opportunity to better understand the causative role of genes in complex biological processes. In this Perspective, we delineate the application of causal machine learning to single-cell genomics and its associated challenges. We first present the causal model that is most commonly applied to single-cell biology and then identify and discuss potential approaches to three open problems: the lack of generalization of models to novel experimental conditions, the complexity of interpreting learned models, and the difficulty of learning cell dynamics.
Indexed as
Identifiers
40164735What OpenQuestion 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 OpenQuestion graph.