ArticleGenome biology2026
VIST: variational inference for single cell time series.
Article in Genome biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- VIST: variational inference for single cell time series.Genome biology · 2026Article
- Emergent multiscale dynamics in photonic neurons with dual feedback.Scientific reports · 2025Article
- Identification of Distinct Topological Structures From High-Dimensional Data.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
- Update of
Authors and funding
2 authors.
Funding
Abstract
Time course single-cell RNA sequencing (scRNA-seq) enables researchers to probe expression dynamics at the resolution of individual cells. Analyzing this data poses several challenges, including deconvolving the contributions of time and cell type, discriminating true dynamics from batch effects, and inferring per-cell dynamics despite cells being destroyed at each time point. We present VIST (Variational Inference for Single cell Time series), a deep-learning algorithm that deconvolves single-cell time series into time-dependent and time-independent components. Using both synthetic and real scRNA-seq data, we show that VIST constructs biologically meaningful latent spaces, removes batch effects, and generates realistic single-cell time series.
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.