ArticleNature communications2023
Explainable multi-task learning for multi-modality biological data analysis.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 1 of them a synthesis that pooled it.
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Who cites it
58 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- GRAIL-heart: A graph attention network for inferring ligand-receptor interactions in spatial transcriptomics.MethodsX · 2026Article
- scTransMIL bridges patient-level disease states and single-cell transcriptomics for cancer screening and heterogeneity inference.Nature communications · 2026Article
- TMO-Net+: An Enhanced Tumor Multi-Omics Pre-Trained Network for Multi-Task Learning in Oncology.Genes · 2026Article
- Artificial intelligence-driven advancements in agricultural biotechnology.Journal, genetic engineering & biotechnology · 2026Review
- scProtoTransformer: Scalable reference mapping across molecules, cells, and donors.Science advances · 2026Article
- A biomimetic, ultralow-power edge-AI-empowered and self-sustaining gait analysis system.Science advances · 2026Article
- scDAU: a disentangled representation learning method for cross-modal translation in single-cell multi-omics data.Bioinformatics (Oxford, England) · 2026Article
- High-resolution reconstruction of cell-type-specific transcriptional regulatory processes from bulk sequencing samples.Nature biotechnology · 2026Article
- Dual-SVF: a robust knowledge-guided multimodal method for structural variant filtering in long-read sequencing.Briefings in bioinformatics · 2026Article
- Artificial intelligence for design strategies of tissue engineering materials.Fundamental research · 2026Review
- Organoids, organ-on-a-chip, and microtumors: Biomimetic 3D tumor models advancing drug development and precision medicine.Acta pharmaceutica Sinica. B · 2026Review
- Physics-Informed Machine Learning in Biomedical Science and Engineering.Annual review of biomedical engineering · 2026Review
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- Therapeutic Gases in Biomedicine: Updates on Nitric Oxide and Beyond.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- A deep adversarial network model for multi-task analysis of single-cell omics data.Briefings in bioinformatics · 2026Article
- Artificial intelligence as a surrogate brain: bridging neural dynamical models and data.National science review · 2026Review
- Multisensory Neuromorphic Devices: From Physics to Integration.Nano-micro letters · 2026Review
- Controllable diffusion-based generation for multi-channel biological data.... International Conference on Learning Representations · 2026Article
- Inflammasomes meet organoids and artificial intelligence: unraveling the complexity of gynecological inflammation.Frontiers in immunology · 2026Review
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
Current biotechnologies can simultaneously measure multiple high-dimensional modalities (e.g., RNA, DNA accessibility, and protein) from the same cells. A combination of different analytical tasks (e.g., multi-modal integration and cross-modal analysis) is required to comprehensively understand such data, inferring how gene regulation drives biological diversity and functions. However, current analytical methods are designed to perform a single task, only providing a partial picture of the multi-modal data. Here, we present UnitedNet, an explainable multi-task deep neural network capable of integrating different tasks to analyze single-cell multi-modality data. Applied to various multi-modality datasets (e.g., Patch-seq, multiome ATAC + gene expression, and spatial transcriptomics), UnitedNet demonstrates similar or better accuracy in multi-modal integration and cross-modal prediction compared with state-of-the-art methods. Moreover, by dissecting the trained UnitedNet with the explainable machine learning algorithm, we can directly quantify the relationship between gene expression and other modalities with cell-type specificity. UnitedNet is a comprehensive end-to-end framework that could be broadly applicable to single-cell multi-modality biology. This framework has the potential to facilitate the discovery of cell-type-specific regulation kinetics across transcriptomics and other modalities.
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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.