ReviewGenomics, proteomics & bioinformatics2022
Application of Deep Learning on Single-cell RNA Sequencing Data Analysis: A Review.
Review in Genomics, proteomics & bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
47 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
- Machine learning and computational approaches to model therapeutic response and resistance in diffuse midline glioma.Journal of neuro-oncology · 2026Review
- Transformers for single-cell RNA sequencing: a survey.Briefings in bioinformatics · 2026Review
- A sequence knowledge-guided deep learning method for single-cell multi-omics translation.Genome biology · 2026Article
- Article
- Interpretable Aging Signatures in Human Retinal Cell Types Revealed by Single-Cell RNA Sequencing and Sparse Logistic Regression.Ophthalmology science · 2026Article
- Prior-guided factorization for reliable imputation of scRNA-seq data.PLoS computational biology · 2026Article
- scSCCNIA: similarity matrix based contrastive clustering with neighbor information aggregation for single-cell RNA sequencing data.Briefings in bioinformatics · 2026Article
- Revealing hidden regulatory dependencies: multi-perspective graph learning for single-cell gene regulatory network inference.Briefings in bioinformatics · 2026Article
- A Deep Learning Approach to Assessing Cell Identity in Stem Cell-Based Embryo Models.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Machine learning approaches for biomarker discovery using single-cell RNA sequencing.Frontiers in bioinformatics · 2026Review
- Cell-type-specific alkaloid and terpenoid biosynthesis in glandular trichomes: single-cell and spatial transcriptomic perspectives.Frontiers in plant science · 2026Review
- A copula-infused graph neural network for cell type classification in single-cell RNA sequencing data.Computational and structural biotechnology journal · 2026Article
- Single-cell sequencing technology in renal cancer: insights into tumor biology and clinical application.Biomarker research · 2025Review
- Interpretable Transfer Learning for Cancer Drug Resistance: Candidate Target Identification.Current issues in molecular biology · 2025Article
- A hybrid adversarial autoencoder-graph network model with dynamic fusion for robust scRNA-seq clustering.BMC genomics · 2025Article
- Global trends in machine learning applications for single-cell transcriptomics research.Hereditas · 2025Article
- Decoding metastatic microenvironments through single-cell omics reveals new insights into niche dynamics and tumor evolution.PLoS biology · 2025Article
- A robust multi-scale clustering framework for single-cell RNA-seq data analysis.Scientific reports · 2025Article
- scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery.Briefings in bioinformatics · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Single-cell RNA sequencing (scRNA-seq) has become a routinely used technique to quantify the gene expression profile of thousands of single cells simultaneously. Analysis of scRNA-seq data plays an important role in the study of cell states and phenotypes, and has helped elucidate biological processes, such as those occurring during the development of complex organisms, and improved our understanding of disease states, such as cancer, diabetes, and coronavirus disease 2019 (COVID-19). Deep learning, a recent advance of artificial intelligence that has been used to address many problems involving large datasets, has also emerged as a promising tool for scRNA-seq data analysis, as it has a capacity to extract informative and compact features from noisy, heterogeneous, and high-dimensional scRNA-seq data to improve downstream analysis. The present review aims at surveying recently developed deep learning techniques in scRNA-seq data analysis, identifying key steps within the scRNA-seq data analysis pipeline that have been advanced by deep learning, and explaining the benefits of deep learning over more conventional analytic tools. Finally, we summarize the challenges in current deep learning approaches faced within scRNA-seq data and discuss potential directions for improvements in deep learning algorithms for scRNA-seq data analysis.
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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.