ArticleBMC biology2025
iVAE: an interpretable representation learning framework enhances clustering performance for single-cell data.
Article in BMC biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Compact and informative representation learning for scRNA-seq data clustering with masked information bottleneck.BMC biology · 2026Article
- LAIOR: a hyperbolic neural ODE variational framework for interpretable single-cell manifold learning and trajectory inference.Frontiers in genetics · 2026Article
- Inductive bias influences the spatial scale of biological features learned from images.Bioinformatics advances · 2026Article
- ScGeo reveals non-canonical trajectories beyond RNA velocity in radiation-induced hematopoietic recovery.Bioinformatics advances · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundVariational autoencoders (VAEs) serve as essential components in large generative models for extracting latent representations and have gained widespread application in biological domains. Developing VAEs specifically tailored to the unique characteristics of biological data is crucial for advancing future large-scale biological models.
resultsThrough systematic monitoring of VAE training processes across 31 public single-cell datasets spanning oncological and normal conditions, we discovered that reducing the
conclusionsThe proposed iVAE architecture enhances the interpretability of single-cell data compared to conventional VAE architectures as measured by clustering metrics. Our work establishes a potential foundational VAE architecture for developing specialized large-scale generative models for biological applications.
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.