ArticleNucleic acids research2017
Using neural networks for reducing the dimensions of single-cell RNA-Seq data.
Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
89 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Visible neural networks for multi-omics integration: a critical review.Frontiers in artificial intelligence · 2025Pooled it
- Human genetics across levels of biological organization.Nature reviews. Genetics · 2026Review
- Self-supervised graph contrastive learning for scRNA-seq clustering.Journal of translational medicine · 2026Article
- HINN: Hierarchical Input Neural Network identifies multi-omics biomarker for cognitive decline.Research square · 2025Article
- Harnessing agent-based frameworks in CellAgentChat to unravel cell-cell interactions from single-cell and spatial transcriptomics.Genome research · 2025Article
- Bioinformatics perspectives on transcriptomics: A comprehensive review of bulk and single-cell RNA sequencing analyses.Quantitative biology (Beijing, China) · 2025Review
- Scaling transformers to high-dimensional sparse data: a Reformer-BERT approach for large-scale classification.Frontiers in artificial intelligence · 2025Article
- An interpretable survival model for diffuse large B-cell lymphoma patients using a biologically informed visible neural network.Computational and structural biotechnology journal · 2024Article
- Latent space arithmetic on data embeddings from healthy multi-tissue human RNA-seq decodes disease modules.Patterns (New York, N.Y.) · 2024Article
- Effectiveness of Artificial Intelligence Technologies in Cancer Treatment for Older Adults: A Systematic Review.Journal of clinical medicine · 2024Review
- Multiscale differential geometry learning of networks with applications to single-cell RNA sequencing data.Computers in biology and medicine · 2024Article
- Deep learning in spatially resolved transcriptfomics: a comprehensive technical viewBriefings in bioinformatics · 2024Article
- A review of artificial intelligence-assisted omics techniques in plant defense: current trends and future directions.Frontiers in plant science · 2024Review
- scFED: Clustering Identifying Cell Types of scRNA-Seq Data Based on Feature Engineering Denoising.Interdisciplinary sciences, computational life sciences · 2023Article
- Vis-SPLIT: Interactive Hierarchical Modeling for mRNA Expression Classification.IEEE Visualization Conference : VIS. IEEE Conference on Visualization · 2023Article
- Review of single-cell RNA-seq data clustering for cell-type identification and characterization.RNA (New York, N.Y.) · 2023Review
- SigPrimedNet: A Signaling-Informed Neural Network for scRNA-seq Annotation of Known and Unknown Cell Types.Biology · 2023Article
- Integration of Computational Analysis and Spatial Transcriptomics in Single-cell Studies.Genomics, proteomics & bioinformatics · 2023Review
- MulCNN: An efficient and accurate deep learning method based on gene embedding for cell type identification in single-cell RNA-seq data.Frontiers in genetics · 2023Article
- Trends and Potential of Machine Learning and Deep Learning in Drug Study at Single-Cell Level.Research (Washington, D.C.) · 2023Review
29 more citing papers are in PubMed but not listed here.
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
No grant is acknowledged in the PubMed record.
Abstract
While only recently developed, the ability to profile expression data in single cells (scRNA-Seq) has already led to several important studies and findings. However, this technology has also raised several new computational challenges. These include questions about the best methods for clustering scRNA-Seq data, how to identify unique group of cells in such experiments, and how to determine the state or function of specific cells based on their expression profile. To address these issues we develop and test a method based on neural networks (NN) for the analysis and retrieval of single cell RNA-Seq data. We tested various NN architectures, some of which incorporate prior biological knowledge, and used these to obtain a reduced dimension representation of the single cell expression data. We show that the NN method improves upon prior methods in both, the ability to correctly group cells in experiments not used in the training and the ability to correctly infer cell type or state by querying a database of tens of thousands of single cell profiles. Such database queries (which can be performed using our web server) will enable researchers to better characterize cells when analyzing heterogeneous scRNA-Seq samples.
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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.