ArticlePatterns (New York, N.Y.)2022
Single-cell multi-modal GAN reveals spatial patterns in single-cell data from triple-negative breast cancer.
Article in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Review
- Beta cell-derived cholecystokinin drives obesity-associated pancreatic adenocarcinoma development.Nature communications · 2026Article
- AAnet Resolves a Continuum of Spatially Localized Cell States to Unveil Intratumoral Heterogeneity.Cancer discovery · 2025Article
- scMODAL: a general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links.Nature communications · 2025Article
- Single-cell analysis reveals transcriptional dynamics in healthy primary parathyroid tissue.Genome research · 2024Article
- Machine learning integrative approaches to advance computational immunology.Genome medicine · 2024Review
- Scalable integration of multiomic single-cell data using generative adversarial networks.Bioinformatics (Oxford, England) · 2024Article
- scmFormer Integrates Large-Scale Single-Cell Proteomics and Transcriptomics Data by Multi-Task Transformer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Prediction of Myocardial Infarction Using a Combined Generative Adversarial Network Model and Feature-Enhanced Loss Function.Metabolites · 2024Article
- Effective multi-modal clustering method via skip aggregation network for parallel scRNA-seq and scATAC-seq data.Briefings in bioinformatics · 2024Article
- The performance of deep generative models for learning joint embeddings of single-cell multi-omics data.Frontiers in molecular biosciences · 2022Review
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
Abstract
Exciting advances in technologies to measure biological systems are currently at the forefront of research. The ability to gather data along an increasing number of omic dimensions has created a need for tools to analyze all of this information together, rather than siloing each technology into separate analysis pipelines. To advance this goal, we introduce a framework called the single-cell multi-modal generative adversarial network (scMMGAN) that integrates data from multiple modalities into a unified representation in the ambient data space for downstream analysis using a combination of adversarial learning and data geometry techniques. The framework's key improvement is an additional diffusion geometry loss with a new kernel that constrains the otherwise over-parameterized GAN. We demonstrate scMMGAN's ability to produce more meaningful alignments than alternative methods on a wide variety of data modalities and that its output can be used to draw conclusions from real-world biological experimental data.
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.