On the public citation graph

Deep learning does not outperform classical machine learning for cell-type annotation

https://doi.org/10.1101/653907
0 signed · 75 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/j.cell.2016.01.047openalex-snapshot
Heterogeneity in Oct4 and Sox2 Targets Biases Cell Fate in 4-Cell Mouse Embryos
inferredcites →https://doi.org/10.1016/j.cell.2016.11.038openalex-snapshot
Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens
inferredcites →https://doi.org/10.1016/j.cell.2018.06.021openalex-snapshot
Molecular Architecture of the Mouse Nervous System
inferredcites →https://doi.org/10.1016/j.cell.2018.07.028openalex-snapshot
Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain
inferredcites →https://doi.org/10.1016/j.cell.2019.05.031openalex-snapshot
Comprehensive Integration of Single-Cell Data
inferredcites →https://doi.org/10.1016/j.cels.2016.08.011openalex-snapshot
A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure
inferredcites →https://doi.org/10.1016/j.cels.2016.09.002openalex-snapshot
A Single-Cell Transcriptome Atlas of the Human Pancreas
inferredcites →https://doi.org/10.1016/j.cmet.2016.08.018openalex-snapshot
RNA Sequencing of Single Human Islet Cells Reveals Type 2 Diabetes Genes
inferredcites →https://doi.org/10.1016/j.cmet.2016.08.020openalex-snapshot
Single-Cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes
inferredcites →https://doi.org/10.1016/j.isci.2020.100882openalex-snapshot
scCATCH: Automatic Annotation on Cell Types of Clusters from Single-Cell RNA Sequencing Data
inferredcites →https://doi.org/10.1016/j.jbi.2013.08.007openalex-snapshot
Learning classification models from multiple experts
inferredcites →https://doi.org/10.1038/nature12364openalex-snapshot
Genetic programs in human and mouse early embryos revealed by single-cell RNA sequencing
inferredcites →https://doi.org/10.1038/nbt.3192openalex-snapshot
Spatial reconstruction of single-cell gene expression data
inferredcites →https://doi.org/10.1038/nbt.4091openalex-snapshot
Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
inferredcites →https://doi.org/10.1038/nbt.4096openalex-snapshot
Integrating single-cell transcriptomic data across different conditions, technologies, and species
inferredcites →https://doi.org/10.1038/nbt.4231openalex-snapshot
Paired-cell sequencing enables spatial gene expression mapping of liver endothelial cells
inferredcites →https://doi.org/10.1038/nmeth.4177openalex-snapshot
Pooled CRISPR screening with single-cell transcriptome readout
inferredcites →https://doi.org/10.1038/nmeth.4197openalex-snapshot
Salmon provides fast and bias-aware quantification of transcript expression
inferredcites →https://doi.org/10.1038/nmeth.4380openalex-snapshot
Simultaneous epitope and transcriptome measurement in single cells
inferredcites →https://doi.org/10.1038/nmeth.4644openalex-snapshot
scmap: projection of single-cell RNA-seq data across data sets
inferredcites →https://doi.org/10.1038/s41467-018-07234-6openalex-snapshot
Discovery of rare cells from voluminous single cell expression data
inferredcites →https://doi.org/10.1038/s41467-018-07931-2openalex-snapshot
Single-cell RNA-seq denoising using a deep count autoencoder
inferredcites →https://doi.org/10.1038/s41467-020-14391-0openalex-snapshot
Surface protein imputation from single cell transcriptomes by deep neural networks
inferredcites →https://doi.org/10.1038/s41467-020-15523-2openalex-snapshot
SciBet as a portable and fast single cell type identifier
inferredcites →https://doi.org/10.1038/s41576-019-0122-6openalex-snapshot
Deep learning: new computational modelling techniques for genomics
inferredcites →https://doi.org/10.1038/s41586-018-0590-4openalex-snapshot
Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris
inferredcites →https://doi.org/10.1038/s41586-019-1184-5openalex-snapshot
Molecular recording of mammalian embryogenesis
inferredcites →https://doi.org/10.1038/s41590-018-0276-yopenalex-snapshot
Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage
inferredcites →https://doi.org/10.1038/s41592-018-0229-2openalex-snapshot
Deep generative modeling for single-cell transcriptomics
inferredcites →https://doi.org/10.1038/s41592-019-0494-8openalex-snapshot
scGen predicts single-cell perturbation responses
inferredcites →https://doi.org/10.1038/s41592-019-0535-3openalex-snapshot
Supervised classification enables rapid annotation of cell atlases
inferredcites →https://doi.org/10.1038/s41592-021-01336-8openalex-snapshot
Benchmarking atlas-level data integration in single-cell genomics
inferredcites →https://doi.org/10.1093/bioinformatics/btz292openalex-snapshot
scMatch: a single-cell gene expression profile annotation tool using reference datasets
inferredcites →https://doi.org/10.1101/574723openalex-snapshot
Deep learning of representations for transcriptomics-based phenotype prediction
inferredcites →https://doi.org/10.1101/gr.177725.114openalex-snapshot
Cell fate inclination within 2-cell and 4-cell mouse embryos revealed by single-cell RNA sequencing
inferredcites →https://doi.org/10.1126/science.1245316openalex-snapshot
Single-Cell RNA-Seq Reveals Dynamic, Random Monoallelic Gene Expression in Mammalian Cells
inferredcites →https://doi.org/10.1126/science.aam8999openalex-snapshot
Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding
inferredcites →https://doi.org/10.1126/science.aar4362openalex-snapshot
Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1186/s13059-015-0706-1openalex-snapshot
Single-cell RNA-seq transcriptome analysis of linear and circular RNAs in mouse preimplantation embryos
inferredcites →https://doi.org/10.1186/s13059-017-1382-0openalex-snapshot
SCANPY: large-scale single-cell gene expression data analysis
inferredcites →https://doi.org/10.1186/s13059-019-1639-xopenalex-snapshot
CellFishing.jl: an ultrafast and scalable cell search method for single-cell RNA sequencing
inferredcites →https://doi.org/10.1186/s13059-019-1795-zopenalex-snapshot
A comparison of automatic cell identification methods for single-cell RNA sequencing data
inferredcites →https://doi.org/10.18653/v1/n18-1202openalex-snapshot
Deep Contextualized Word Representations
inferredcites →https://doi.org/10.48550/arxiv.1103.0398openalex-snapshot
Natural Language Processing (almost) from Scratch

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/s41060-024-00681-zopenalex-snapshot
Attention versus contrastive learning of tabular data: a data-centric benchmarking
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2022.106409openalex-snapshot
scWECTA: A weighted ensemble classification framework for cell type assignment based on single cell transcriptome
inferred← citeshttps://doi.org/10.1016/j.crmeth.2023.100577openalex-snapshot
Single-cell assignment using multiple-adversarial domain adaptation network with large-scale references
inferred← citeshttps://doi.org/10.1016/j.csbj.2023.07.041openalex-snapshot
Exploring the optimization of autoencoder design for imputing single-cell RNA sequencing data
inferred← citeshttps://doi.org/10.1016/j.neucom.2025.129359openalex-snapshot
Deep clustering of tabular data by weighted Gaussian distribution learning
inferred← citeshttps://doi.org/10.1016/j.neunet.2022.09.020openalex-snapshot
Perturbation of deep autoencoder weights for model compression and classification of tabular data
inferred← citeshttps://doi.org/10.1016/j.stemcr.2022.11.007openalex-snapshot
Deep learning models will shape the future of stem cell research
inferred← citeshttps://doi.org/10.1016/j.xcrm.2024.101535openalex-snapshot
Modeling type 1 diabetes progression using machine learning and single-cell transcriptomic measurements in human islets
inferred← citeshttps://doi.org/10.1038/s41467-024-51059-5openalex-snapshot
scTab: Scaling cross-tissue single-cell annotation models
inferred← citeshttps://doi.org/10.1038/s41592-023-02035-2openalex-snapshot
Population-level integration of single-cell datasets enables multi-scale analysis across samples
inferred← citeshttps://doi.org/10.1038/s41592-024-02353-zopenalex-snapshot
Transformers in single-cell omics: a review and new perspectives
inferred← citeshttps://doi.org/10.1038/s41598-023-49048-7openalex-snapshot
Enhancing autism spectrum disorder classification in children through the integration of traditional statistics and classical machine learning techniques in EEG analysis
inferred← citeshttps://doi.org/10.1093/bib/bbaf239openalex-snapshot
Evaluation of out-of-distribution detection methods for data shifts in single-cell transcriptomics
inferred← citeshttps://doi.org/10.1093/nargab/lqab102openalex-snapshot
Besca, a single-cell transcriptomics analysis toolkit to accelerate translational research
inferred← citeshttps://doi.org/10.1093/nargab/lqag095openalex-snapshot
A systematic assessment of single-cell language model configurations
inferred← citeshttps://doi.org/10.1101/2020.05.28.121541openalex-snapshot
Alterations of multiple alveolar macrophage states in chronic obstructive pulmonary disease
inferred← citeshttps://doi.org/10.1101/2021.04.28.441762openalex-snapshot
Cross-tissue immune cell analysis reveals tissue-specific adaptations and clonal architecture in humans
inferred← citeshttps://doi.org/10.1101/2023.05.15.540808openalex-snapshot
MASIv2 enables standardization and integration of multi-modal single-cell and spatial omics data with one general framework
inferred← citeshttps://doi.org/10.1101/2023.10.07.561331openalex-snapshot
Scaling cross-tissue single-cell annotation models
inferred← citeshttps://doi.org/10.1101/655365openalex-snapshot
Accurate denoising of single-cell RNA-Seq data using unbiased principal component analysis
inferred← citeshttps://doi.org/10.1109/icaci58115.2023.10146161openalex-snapshot
Effectiveness of Deep Image Embedding Clustering Methods on Tabular Data
inferred← citeshttps://doi.org/10.1109/icece64886.2024.11024938openalex-snapshot
Transfer Learning of Tabular Data by Finetuning Large Language Models
inferred← citeshttps://doi.org/10.1126/science.abl5197openalex-snapshot
Cross-tissue immune cell analysis reveals tissue-specific features in humans
inferred← citeshttps://doi.org/10.1186/s13059-019-1795-zopenalex-snapshot
A comparison of automatic cell identification methods for single-cell RNA sequencing data
inferred← citeshttps://doi.org/10.1186/s13059-020-02100-5openalex-snapshot
Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data
inferred← citeshttps://doi.org/10.1186/s13059-021-02452-6openalex-snapshot
Sfaira accelerates data and model reuse in single cell genomics
inferred← citeshttps://doi.org/10.12688/f1000research.22969.1openalex-snapshot
clustifyr: an R package for automated single-cell RNA sequencing cluster classification
inferred← citeshttps://doi.org/10.33612/diss.255266474openalex-snapshot
Context matters
inferred← citeshttps://doi.org/10.64898/2026.01.06.697922openalex-snapshot
HEDeST: An Integrative Approach to Enhance Spatial Transcriptomic Deconvolution with Histology
inferred← citeshttps://doi.org/10.7554/elife.52155openalex-snapshot
The single-cell eQTLGen consortium
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.