ReviewGenome biology2021
Feature selection revisited in the single-cell era.
Review in Genome biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
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.
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Who cites it
50 citing papers in PubMed, 88 citations in OpenAlex.
- A graph-attentive GAN for rare-cell-aware single-cell RNA-seq data generation.PLoS computational biology · 2026Article
- BaGGLS: a Bayesian shrinkage framework for interpretable modeling of interactions in high-dimensional biological data.Bioinformatics (Oxford, England) · 2026Article
- Interpretable deep generative ensemble learning for single-cell omics with Hydra.Molecular systems biology · 2026Article
- Integrating host-microbiome multi-omics with machine learning: methods, benchmarks, and translational applications.Science China. Life sciences · 2026Review
- Systematic clustering alignment and feature characterization for single-cell omics using ACE-OF-Clust.bioRxiv : the preprint server for biology · 2026Article
- Unveiling patterns: an exploration of machine learning techniques for unsupervised feature selection in single-cell data.Briefings in bioinformatics · 2026Review
- Quantum annealing for enhanced feature selection in single-cell RNA sequencing data analysis.Quantum machine intelligence · 2025Article
- Multitask benchmarking of single-cell multimodal omics integration methods.Nature methods · 2025Article
- Computational design and evaluation of optimal bait sets for scalable proximity proteomics.Nature communications · 2025Article
- Computational tracking of cell origins using CellSexID from single-cell transcriptomes.Cell reports methods · 2025Article
- Multi-view gene panel characterization for spatially resolved omics.Briefings in bioinformatics · 2025Article
- Sketching T cell atlases in the single-cell era: challenges and recommendations.Immunology and cell biology · 2025Review
- Cross study transcriptomic investigation of Alzheimer's brain tissue discoveries and limitations.Scientific reports · 2025Article
- Acute Myeloid Leukemia Genome Characterization Study and Subtype Classification Employing Feature Selection and Bayesian Networks.Biomedicines · 2025Article
- Inorganic pyrophosphatase 1: a key player in immune and metabolic reprogramming in ankylosing spondylitis.Genes and immunity · 2025Article
- Considerations for building and using integrated single-cell atlases.Nature methods · 2025Review
- CosGeneGate selects multi-functional and credible biomarkers for single-cell analysis.Briefings in bioinformatics · 2024Article
- scPair: Boosting single cell multimodal analysis by leveraging implicit feature selection and single cell atlases.Nature communications · 2024Article
- A phenotypic drug discovery approach by latent interaction in deep learning.Royal Society open science · 2024Article
- Inference of single-cell network using mutual information for scRNA-seq data analysis.BMC bioinformatics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advances in single-cell biotechnologies have resulted in high-dimensional datasets with increased complexity, making feature selection an essential technique for single-cell data analysis. Here, we revisit feature selection techniques and summarise recent developments. We review their application to a range of single-cell data types generated from traditional cytometry and imaging technologies and the latest array of single-cell omics technologies. We highlight some of the challenges and future directions and finally consider their scalability and make general recommendations on each type of feature selection method. We hope this review stimulates future research and application of feature selection in the single-cell era.
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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 Socratic graph.