ArticleNature biotechnology2024
Disentanglement of single-cell data with biolord.
Article in Nature biotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
36 citing papers in PubMed.
- Disentangling covariate effects on single-cell-resolved epigenomes with DeepDive.Patterns (New York, N.Y.) · 2026Article
- A systematic comparison of single-cell perturbation response prediction models.Science advances · 2026Article
- What-if effects: A tale of entangled covariates.Patterns (New York, N.Y.) · 2026Article
- DECANT: decoupling mechanism from context in single-cell drug perturbation representation.Bioinformatics (Oxford, England) · 2026Article
- scBalFlow: a staged flow matching framework for imbalanced single-cell drug perturbation prediction.Bioinformatics (Oxford, England) · 2026Article
- scDIVA: semi-supervised integration and fine-grained annotation of tumor-immune single-cell atlases.bioRxiv : the preprint server for biology · 2026Article
- Single-cell ultra-high-throughput multiplexed chromatin accessibility and gene expression sequencing (SUM-seq).Nature protocols · 2026Review
- A generative framework for predicting cellular morphological and transcriptomic perturbation responses.Cell reports methods · 2026Article
- Article
- Benchmarking the prediction of responding cells to perturbations affecting both gene expression and cellular abundance using scRNA sequencing.Scientific reports · 2026Article
- Semi-supervised disentangled representation learning for single-cell RNA sequencing data.Briefings in bioinformatics · 2026Article
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- Combinatorial prediction of therapeutic perturbations using causally inspired neural networks.Nature biomedical engineering · 2026Article
- iAODE for benchmarking and continuum modeling of single-cell chromatin accessibility.Communications biology · 2026Article
- Predicting drug-perturbed transcriptional responses using multi-conditional diffusion transformer.Quantitative biology (Beijing, China) · 2026Article
- Benchmarking algorithms for generalizable single-cell perturbation response prediction.Nature methods · 2026Article
- Toward trustworthy virtual cells: a roadmap for perturbation-resolved, context-aware, and experimentally validated cell models.Frontiers in cell and developmental biology · 2026Review
- Scouter predicts transcriptional responses to genetic perturbations with large language model embeddings.Nature computational science · 2026Article
- Scaling Large Language Models for Next-Generation Single-Cell Analysis.bioRxiv : the preprint server for biology · 2026Article
- Interpretable phenotype decoding from multicondition sequencing data with ALPINE.Genome research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biolord is a deep generative method for disentangling single-cell multi-omic data to known and unknown attributes, including spatial, temporal and disease states, used to reveal the decoupled biological signatures over diverse single-cell modalities and biological systems. By virtually shifting cells across states, biolord generates experimentally inaccessible samples, outperforming state-of-the-art methods in predictions of cellular response to unseen drugs and genetic perturbations. Biolord is available at https://github.com/nitzanlab/biolord .
Indexed as
Identifiers
What 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.