ArticleBriefings in bioinformatics2024
scMMT: a multi-use deep learning approach for cell annotation, protein prediction and embedding in single-cell RNA-seq data.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 12 citations in OpenAlex.
- Specificity-driven cell-gene graph learning identifies rare cell states in single-cell and spatial transcriptomic data.Advanced biotechnology · 2026Article
- Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- ADTnorm: robust integration of single-cell protein measurement across CITE-seq datasets.Nature communications · 2025Article
- ProtGO: universal protein function prediction utilizing multi-modal gene ontology knowledge.Bioinformatics (Oxford, England) · 2025Article
- An overview of computational methods in single-cell transcriptomic cell type annotation.Briefings in bioinformatics · 2025Review
- A joint analysis of single cell transcriptomics and proteomics using transformer.NPJ systems biology and applications · 2025Article
- Computational identification of surface markers for isolating distinct subpopulations from heterogeneous cancer cell populations.NPJ systems biology and applications · 2024Article
- ADTnorm: Robust Integration of Single-cell Protein Measurement across CITE-seq Datasets.Research square · 2024Article
- Computational identification of surface markers for isolating distinct subpopulations from heterogeneous cancer cell populations.bioRxiv : the preprint server for biology · 2024Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Accurate cell type annotation in single-cell RNA-sequencing data is essential for advancing biological and medical research, particularly in understanding disease progression and tumor microenvironments. However, existing methods are constrained by single feature extraction approaches, lack of adaptability to immune cell types with similar molecular profiles but distinct functions and a failure to account for the impact of cell label noise on model accuracy, all of which compromise the precision of annotation. To address these challenges, we developed a supervised approach called scMMT. We proposed a novel feature extraction technique to uncover more valuable information. Additionally, we constructed a multi-task learning framework based on the GradNorm method to enhance the recognition of challenging immune cells and reduce the impact of label noise by facilitating mutual reinforcement between cell type annotation and protein prediction tasks. Furthermore, we introduced logarithmic weighting and label smoothing mechanisms to enhance the recognition ability of rare cell types and prevent model overconfidence. Through comprehensive evaluations on multiple public datasets, scMMT has demonstrated state-of-the-art performance in various aspects including cell type annotation, rare cell identification, dropout and label noise resistance, protein expression prediction and low-dimensional embedding representation.
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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.