ReviewFrontiers in genetics2021
Machine Intelligence in Single-Cell Data Analysis: Advances and New Challenges.
Review in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
38 citing papers in PubMed.
- Bayesian hyperparameter optimization improves scGPT fine-tuning for single-cell multi-omics integration.Bioinformatics (Oxford, England) · 2026Article
- Deciphering Cell Cycle Dynamics and Cell States in Single-cell RNA-seq data with SPAE.bioRxiv : the preprint server for biology · 2026Article
- Single-Nucleus RNA Sequencing Reveals Mid-Gestational Neurodevelopment Features in the Superior Temporal Plane from Fetuses with Nonsyndromic Cleft Lip and Palate.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Bioinformatics Approaches in Noncoding RNAs Research.Experientia supplementum (2012) · 2026Review
- Decoding heart failure subtypes with neural networks via differential explanation analysis.Briefings in bioinformatics · 2025Article
- Global trends in machine learning applications for single-cell transcriptomics research.Hereditas · 2025Article
- Single cell RNA sequencing improves the next generation of approaches to AML treatment: challenges and perspectives.Molecular medicine (Cambridge, Mass.) · 2025Review
- Spatial profiling of the metabolism-immune axis in ovarian cancer.Frontiers in pharmacology · 2025Review
- scGO: interpretable deep neural network for cell status annotation and disease diagnosis.Briefings in bioinformatics · 2024Article
- Gene expression clock: an unsupervised deep learning approach for predicting circadian rhythmicity from whole genome expression.Neural computing & applications · 2024Article
- Review
- Mouse Corneal Immune Cell Heterogeneity Revealed by Single-Cell RNA Sequencing.Investigative ophthalmology & visual science · 2024Article
- Cancer marker TNFRSF1A: From single‑cell heterogeneity of renal cell carcinoma to functional validation.Oncology letters · 2024Article
- scGPT: toward building a foundation model for single-cell multi-omics using generative AI.Nature methods · 2024Article
- Applications of single‑cell omics and spatial transcriptomics technologies in gastric cancer (Review).Oncology letters · 2024Review
- Single-Cell RNA Sequencing and Its Applications in Pituitary Research.Neuroendocrinology · 2024Review
- A machine learning one-class logistic regression model to predict stemness for single cell transcriptomics and spatial omics.BMC genomics · 2023Article
- Machine Learning Analysis of Alzheimer's Disease Single-Cell RNA-Sequencing Data across Cortex and Hippocampus Regions.Current issues in molecular biology · 2023Article
- Multimodal deep learning approaches for single-cell multi-omics data integration.Briefings in bioinformatics · 2023Review
- LVPT: Lazy Velocity Pseudotime Inference Method.Biomolecules · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The rapid development of single-cell technologies allows for dissecting cellular heterogeneity at different omics layers with an unprecedented resolution. In-dep analysis of cellular heterogeneity will boost our understanding of complex biological systems or processes, including cancer, immune system and chronic diseases, thereby providing valuable insights for clinical and translational research. In this review, we will focus on the application of machine learning methods in single-cell multi-omics data analysis. We will start with the pre-processing of single-cell RNA sequencing (scRNA-seq) data, including data imputation, cross-platform batch effect removal, and cell cycle and cell-type identification. Next, we will introduce advanced data analysis tools and methods used for copy number variance estimate, single-cell pseudo-time trajectory analysis, phylogenetic tree inference, cell-cell interaction, regulatory network inference, and integrated analysis of scRNA-seq and spatial transcriptome data. Finally, we will present the latest analyzing challenges, such as multi-omics integration and integrated analysis of scRNA-seq data.
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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.