ArticlePLoS computational biology2024
scMoMtF: An interpretable multitask learning framework for single-cell multi-omics data analysis.
Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Topology-guided dual-masked contrastive learning for weakly supervised cell-type annotation in single-cell multi-omics.Briefings in bioinformatics · 2026Article
- SRLST: a unified multimodal representation learning framework for spatial transcriptomics analysis.Bioinformatics (Oxford, England) · 2026Article
- AICellType: a large language model-based platform for accurate cell type annotation.Briefings in bioinformatics · 2026Article
- An unsupervised method for spatial transcriptomics analysis based on adversarial autoencoder.Briefings in bioinformatics · 2026Article
- MOMHCA-SG: a multi-head cross-attention and similar graph convolutional network framework for Alzheimer's disease cell type classification.Frontiers in neuroscience · 2026Article
- A comprehensive study based on large-sample multi-omics integration and machine learning to decode mitochondria-associated genes: from digestive tract tumours to gastric cancer.Frontiers in immunology · 2026Article
- Integrated multi-omics and machine learning identify EFNA3 as a key biomarker of tumor invasion.Frontiers in immunology · 2026Article
- MULGONET: An interpretable neural network framework to integrate multi-omics data for cancer recurrence prediction and biomarker discovery.Fundamental research · 2026Article
- SuperGLUE facilitates an explainable training framework for multi-modal data analysis.Cell reports methods · 2025Article
- scHSC: enhancing single-cell RNA-seq clustering via hard sample contrastive learning.Briefings in bioinformatics · 2025Article
- scAGCI: an anchor graph-based method for cell clustering from integrated scRNA-seq and scATAC-seq data.Briefings in bioinformatics · 2025Article
- Securing diagonal integration of multimodal single-cell data against ambiguous mapping.Bioinformatics (Oxford, England) · 2025Article
- Fractional Order Total Variation Low-Rank Representation on Single-Cell RNA Sequencing Clustering.IET systems biologyArticle
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Authors and funding
6 authors.
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Abstract
With the rapidly development of biotechnology, it is now possible to obtain single-cell multi-omics data in the same cell. However, how to integrate and analyze these single-cell multi-omics data remains a great challenge. Herein, we introduce an interpretable multitask framework (scMoMtF) for comprehensively analyzing single-cell multi-omics data. The scMoMtF can simultaneously solve multiple key tasks of single-cell multi-omics data including dimension reduction, cell classification and data simulation. The experimental results shows that scMoMtF outperforms current state-of-the-art algorithms on these tasks. In addition, scMoMtF has interpretability which allowing researchers to gain a reliable understanding of potential biological features and mechanisms in single-cell multi-omics data.
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