ArticlePLoS computational biology2021
Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations on single cell data.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed, 5 citations in OpenAlex.
- dioscRi enables transferable prediction of clinical outcomes in multi-parameter cytometry data.Nature communications · 2026Article
- A scalable, multi-resolution consensus clustering approach for prioritizing robust signals from high-throughput screens.Briefings in bioinformatics · 2026Article
- Generative design of synthetic gene circuits for functional and evolutionary properties.NPJ systems biology and applications · 2026Article
- Integrating single-cell RNA-seq datasets with substantial batch effects.BMC genomics · 2025Article
- Formulation Strategies for Immunomodulatory Natural Products in 3D Tumor Spheroids and Organoids: Current Challenges and Emerging Solutions.Pharmaceutics · 2025Review
- IGCLAPS: an interpretable graph contrastive learning method with adaptive positive sampling for scRNA-seq data analysis.Bioinformatics (Oxford, England) · 2025Article
- A variational deep-learning approach to modeling memory T cell dynamics.PLoS computational biology · 2025Article
- A variational deep-learning approach to modeling memory T cell dynamics.bioRxiv : the preprint server for biology · 2025Article
- Fuzz Testing Molecular Representation Using Deep Variational Anomaly Generation.Journal of chemical information and modeling · 2025Article
- Covariate Adjusted Functional Mixed Membership Models.Statistics and data science in imaging · 2025Article
- Comparing factor mixture modeling and conditional Gaussian mixture variational autoencoders for cognitive profile clustering.Frontiers in psychology · 2025Article
- Integrating single-cell RNA-seq datasets with substantial batch effects.bioRxiv : the preprint server for biology · 2024Article
- Transfer learning for clustering single-cell RNA-seq data crossing-species and batch, case on uterine fibroids.Briefings in bioinformatics · 2023Article
- Unraveling dynamically encoded latent transcriptomic patterns in pancreatic cancer cells by topic modeling.Cell genomics · 2023Article
- Attentive Variational Information Bottleneck for TCR-peptide interaction prediction.Bioinformatics (Oxford, England) · 2023Article
- Application of Deep Learning on Single-cell RNA Sequencing Data Analysis: A Review.Genomics, proteomics & bioinformatics · 2022Review
- Latent representation learning in biology and translational medicine.Patterns (New York, N.Y.) · 2021Review
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 at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Clustering high-dimensional data, such as images or biological measurements, is a long-standing problem and has been studied extensively. Recently, Deep Clustering has gained popularity due to its flexibility in fitting the specific peculiarities of complex data. Here we introduce the Mixture-of-Experts Similarity Variational Autoencoder (MoE-Sim-VAE), a novel generative clustering model. The model can learn multi-modal distributions of high-dimensional data and use these to generate realistic data with high efficacy and efficiency. MoE-Sim-VAE is based on a Variational Autoencoder (VAE), where the decoder consists of a Mixture-of-Experts (MoE) architecture. This specific architecture allows for various modes of the data to be automatically learned by means of the experts. Additionally, we encourage the lower dimensional latent representation of our model to follow a Gaussian mixture distribution and to accurately represent the similarities between the data points. We assess the performance of our model on the MNIST benchmark data set and challenging real-world tasks of clustering mouse organs from single-cell RNA-sequencing measurements and defining cell subpopulations from mass cytometry (CyTOF) measurements on hundreds of different datasets. MoE-Sim-VAE exhibits superior clustering performance on all these tasks in comparison to the baselines as well as competitor methods.
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.