ArticleNature communications2024
scSemiProfiler: Advancing large-scale single-cell studies through semi-profiling with deep generative models and active learning.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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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.
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Who cites it
18 citing papers in PubMed.
- Discovering reference-missing cell types in bulk transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- Integrative machine learning analysis suggests novel molecular targets for liver cancer diagnosis and therapy.Discover oncology · 2026Article
- SIDISH integrates single-cell and bulk transcriptomics to identify high-risk cells and guide precision therapeutics through in silico perturbation.Nature communications · 2025Article
- Kidney diseases and single-cell sequencing research: a bibliometric analysis from 2015 to 2024.Renal failure · 2025Review
- A deep generative model for deciphering cellular dynamics and in silico drug discovery in complex diseases.Nature biomedical engineering · 2025Article
- MOADE: a multimodal autoencoder for dissociating bulk multi-omics data.Genome biology · 2025Article
- Interpretable Transfer Learning for Cancer Drug Resistance: Candidate Target Identification.Current issues in molecular biology · 2025Article
- Unveiling tissue-specific transcriptional adaptations in iPSC-derived fibroblasts via co-culture systems.Stem cell research & therapy · 2025Article
- DOLPHIN advances single-cell transcriptomics beyond gene level by leveraging exon and junction reads.Nature communications · 2025Article
- Approaching the holistic transcriptome-convolution and deconvolution in transcriptomics.Briefings in bioinformatics · 2025Review
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- Harnessing agent-based frameworks in CellAgentChat to unravel cell-cell interactions from single-cell and spatial transcriptomics.Genome research · 2025Article
- scATD: a high-throughput and interpretable framework for single-cell cancer drug resistance prediction and biomarker identification.Briefings in bioinformatics · 2025Article
- Deep learning based deconvolution methods: A systematic review.Computational and structural biotechnology journal · 2025Review
- Editorial: The Human Cell Atlas. What Is It and Where Could It Take Us?Medical science monitor : international medical journal of experimental and clinical research · 2025Article
- Applications of AI to single-cell and spatial transcriptomics: current state-of-the-art and challenges.Frontiers in bioinformatics · 2025Review
- Computational technologies of the Human Cell Atlas.Nature · 2024Article
- scSemiProfiler: Advancing large-scale single-cell studies through semi-profiling with deep generative models and active learning.Nature communications · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Single-cell sequencing is a crucial tool for dissecting the cellular intricacies of complex diseases. Its prohibitive cost, however, hampers its application in expansive biomedical studies. Traditional cellular deconvolution approaches can infer cell type proportions from more affordable bulk sequencing data, yet they fall short in providing the detailed resolution required for single-cell-level analyses. To overcome this challenge, we introduce "scSemiProfiler", an innovative computational framework that marries deep generative models with active learning strategies. This method adeptly infers single-cell profiles across large cohorts by fusing bulk sequencing data with targeted single-cell sequencing from a few rigorously chosen representatives. Extensive validation across heterogeneous datasets verifies the precision of our semi-profiling approach, aligning closely with true single-cell profiling data and empowering refined cellular analyses. Originally developed for extensive disease cohorts, "scSemiProfiler" is adaptable for broad applications. It provides a scalable, cost-effective solution for single-cell profiling, facilitating in-depth cellular investigation in various biological domains.
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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.