ArticleNucleic acids research2023
Multi-task learning from multimodal single-cell omics with Matilda.
Article in Nucleic acids research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed.
- scGSI: Graph-guided self-supervised integration of paired single-cell multi-omics.PLoS computational biology · 2026Article
- Topology-guided dual-masked contrastive learning for weakly supervised cell-type annotation in single-cell multi-omics.Briefings in bioinformatics · 2026Article
- UDEC-MO: an uncertainty-guided deep embedded clustering framework for bulk and single-cell multi-omics data.Briefings in bioinformatics · 2026Article
- Interpretable deep generative ensemble learning for single-cell omics with Hydra.Molecular systems biology · 2026Article
- Computational blueprints for cell fate programming.Stem cell reports · 2026Review
- Comparative Biofilmomics of Antimicrobial-ResistantAnimals : an open access journal from MDPI · 2026Review
- A deep adversarial network model for multi-task analysis of single-cell omics data.Briefings in bioinformatics · 2026Article
- Review
- CelLink: integrating single-cell multi-omics data with weak feature linkage and imbalanced cell populations.Nucleic acids research · 2025Article
- Multitask benchmarking of single-cell multimodal omics integration methods.Nature methods · 2025Article
- CLUEY enables knowledge-guided clustering and cell type detection from single-cell omics data.Bioinformatics (Oxford, England) · 2025Article
- Multi-view gene panel characterization for spatially resolved omics.Briefings in bioinformatics · 2025Article
- A message passing framework for precise cell state identification with scClassify2.Genome biology · 2025Article
- Semi-supervised contrastive learning variational autoencoder Integrating single-cell multimodal mosaic datasets.BMC bioinformatics · 2025Article
- Ocelli: an open-source tool for the analysis and visualization of developmental multimodal single-cell data.NAR genomics and bioinformatics · 2025Article
- Multimodal hierarchical classification of CITE-seq data delineates immune cell states across lineages and tissues.Cell reports methods · 2025Article
- scMoMtF: An interpretable multitask learning framework for single-cell multi-omics data analysis.PLoS computational biology · 2024Article
- scDRMAE: integrating masked autoencoder with residual attention networks to leverage omics feature dependencies for accurate cell clustering.Bioinformatics (Oxford, England) · 2024Article
- Applying interpretable machine learning in computational biology-pitfalls, recommendations and opportunities for new developments.Nature methods · 2024Review
- Interpretable deep learning in single-cell omics.Bioinformatics (Oxford, England) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multimodal single-cell omics technologies enable multiple molecular programs to be simultaneously profiled at a global scale in individual cells, creating opportunities to study biological systems at a resolution that was previously inaccessible. However, the analysis of multimodal single-cell omics data is challenging due to the lack of methods that can integrate across multiple data modalities generated from such technologies. Here, we present Matilda, a multi-task learning method for integrative analysis of multimodal single-cell omics data. By leveraging the interrelationship among tasks, Matilda learns to perform data simulation, dimension reduction, cell type classification, and feature selection in a single unified framework. We compare Matilda with other state-of-the-art methods on datasets generated from some of the most popular multimodal single-cell omics technologies. Our results demonstrate the utility of Matilda for addressing multiple key tasks on integrative multimodal single-cell omics data analysis. Matilda is implemented in Pytorch and is freely available from https://github.com/PYangLab/Matilda.
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.