Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: May 31, 2026

Analysis of Multidimensional Microscopy Data Using Cell-ACDC
06:17

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

MambaCell: A Self-Supervised Mamba Framework for Multi-Task Cell Representation Learning.

Lu Yu, Yutong Liu, Wensheng Xiang

    IEEE Journal of Biomedical and Health Informatics
    |May 28, 2026
    PubMed
    Summary
    This summary is machine-generated.

    Related Concept Videos

    You might also read

    Related Articles

    Articles linked to this work by shared authors, journal, and citation graph.

    Sort by
    Same author

    Oxidative trifunctionalization of indoles with 2-aminobenzyl alcohols: access to iodo-indoloquinoline scaffolds.

    Organic & biomolecular chemistry·2026
    Same author

    A Novel Approach for the Fabrication of Polarization-Selectivity UV Photosynapse Devices With PdSe<sub>2</sub>/ a-plane GaN Heterostructures.

    Advanced materials (Deerfield Beach, Fla.)·2026
    Same author

    Correction to Rapid Substrate Neutralization via Side-Chain Hydroxylated Copolymers for Efficient Vertical Alignment of Block Copolymers.

    ACS macro letters·2026
    Same author

    TransGAT-DTI: Transformer and Graph Attention Network for Drug-Target Interaction Prediction.

    Journal of computational biology : a journal of computational molecular cell biology·2026
    Same author

    Multi-domain based heterogeneous network for drug-target interaction prediction.

    Artificial intelligence in medicine·2026
    Same author

    Rapid Substrate Neutralization via Side-Chain Hydroxylated Copolymers for Efficient Vertical Alignment of Block Copolymers.

    ACS macro letters·2026
    Same journal

    An Ultra-Lightweight Cross-scale Attention Mamba Network for Accurate Skin Lesion Segmentation.

    IEEE journal of biomedical and health informatics·2026
    Same journal

    Explanation-Guided Reconstruction of Missing Clinical Features for Survival Prediction in Pancreatic Cancer.

    IEEE journal of biomedical and health informatics·2026
    Same journal

    stDGCN: A dual-augmentation graph convolutional network for identifying spatial domains with attention mechanism.

    IEEE journal of biomedical and health informatics·2026
    Same journal

    Patient-specific Biomechanical Investigation of Percutaneous Pulmonary Valves: Towards the Integration of Routinely Acquired Clinical Data and Fluid-structure Interaction Simulations.

    IEEE journal of biomedical and health informatics·2026
    Same journal

    Cross-subject fMRI-to-Image with Visual-cortex 2D Representation and Pre-Training.

    IEEE journal of biomedical and health informatics·2026
    Same journal

    PGCASurv: A Prior-Guided Cross-Attention Framework for Dynamic Survival Model with Longitudinal Data.

    IEEE journal of biomedical and health informatics·2026
    See all related articles

    MambaCell, a new framework for single-cell transcriptomics, uses bidirectional Mamba architecture for efficient cell representation learning. It outperforms transformer models in speed and memory, excelling in tasks like cell type annotation.

    Area of Science:

    • Computational Biology
    • Genomics
    • Artificial Intelligence

    Background:

    • Single-cell RNA sequencing (scRNA-seq) is vital for life sciences.
    • Large Language Models (LLMs) advance cellular foundation models in transcriptomics.
    • Existing transformer-based LLMs face computational limits with long sequences and single-objective learning.

    Purpose of the Study:

    • Introduce MambaCell, an efficient and general framework for scalable cell representation learning.
    • Address limitations of current LLMs in transcriptomics, including computational complexity and restricted representational capacity.
    • Develop a multi-task self-supervised learning framework for robust cell representations from large, unlabeled datasets.

    Main Methods:

    • Developed MambaCell, a framework utilizing bidirectional Mamba architecture.

    Related Experiment Videos

    Last Updated: May 31, 2026

    Analysis of Multidimensional Microscopy Data Using Cell-ACDC
    06:17

    Analysis of Multidimensional Microscopy Data Using Cell-ACDC

    Published on: November 7, 2025

  • Integrated multi-task self-supervised learning with Masked Gene Modeling (MGM) and Contrastive Learning (CL).
  • Enabled learning of cell representations with reduced inference costs on large-scale, unlabeled transcriptomic data.

    • Main Results:

    • MambaCell demonstrated superior or comparable performance to state-of-the-art models on various downstream tasks.
    • Achieved high accuracy in cell type annotation, disease-related cell classification, and single-cell batch integration.
    • Showcased significantly faster inference speed and higher memory efficiency than transformer-based models.

    Conclusions:

    • MambaCell offers a scalable solution for large-scale single-cell transcriptomic analysis.
    • The Mamba architecture and multi-task learning approach effectively capture rich biological information.
    • MambaCell represents a significant advancement in efficient and generalizable cell representation learning.