ArticleBMC bioinformatics2019
Combining gene ontology with deep neural networks to enhance the clustering of single cell RNA-Seq data.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 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
- Expanding canonical cortical cell type markers in the era of single-cell transcriptomics.Scientific reports · 2026Article
- Strategies to include prior knowledge in omics analysis with deep neural networks.Patterns (New York, N.Y.) · 2025Review
- Exploring machine learning strategies for single-cell transcriptomic analysis in wound healing.Burns & trauma · 2025Review
- Biologically informed variational autoencoders allow predictive modeling of genetic and drug-induced perturbations.Bioinformatics (Oxford, England) · 2023Article
- Application of Deep Learning on Single-cell RNA Sequencing Data Analysis: A Review.Genomics, proteomics & bioinformatics · 2022Review
- Deep GONet: self-explainable deep neural network based on Gene Ontology for phenotype prediction from gene expression data.BMC bioinformatics · 2021Article
- A novel method for predicting cell abundance based on single-cell RNA-seq data.BMC bioinformatics · 2021Article
- DRACP: a novel method for identification of anticancer peptides.BMC bioinformatics · 2020Article
- What has single-cell RNA sequencing revealed about microglial neuroimmunology?Immunity, inflammation and disease · 2020Review
- A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network.BMC bioinformatics · 2020Article
- Clustering and classification methods for single-cell RNA-sequencing data.Briefings in bioinformatics · 2020Review
- Predicting the Disease Genes of Multiple Sclerosis Based on Network Representation Learning.Frontiers in genetics · 2020Article
- Disease Module Identification Based on Representation Learning of Complex Networks Integrated From GWAS, eQTL Summaries, and Human Interactome.Frontiers in bioengineering and biotechnology · 2020Article
- Deep Reinforcement Learning for Data Association in Cell Tracking.Frontiers in bioengineering and biotechnology · 2020Article
- CHG: A Systematically Integrated Database of Cancer Hallmark Genes.Frontiers in genetics · 2020Article
- TS-GOEA: a web tool for tissue-specific gene set enrichment analysis based on gene ontology.BMC bioinformatics · 2019Article
- ProbPFP: a multiple sequence alignment algorithm combining hidden Markov model optimized by particle swarm optimization with partition function.BMC bioinformatics · 2019Article
- Extracting a biologically latent space of lung cancer epigenetics with variational autoencoders.BMC bioinformatics · 2019Article
- Identifying Alzheimer's disease-related proteins by LRRGD.BMC bioinformatics · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSingle cell RNA sequencing (scRNA-seq) is applied to assay the individual transcriptomes of large numbers of cells. The gene expression at single-cell level provides an opportunity for better understanding of cell function and new discoveries in biomedical areas. To ensure that the single-cell based gene expression data are interpreted appropriately, it is crucial to develop new computational methods.
resultsIn this article, we try to re-construct a neural network based on Gene Ontology (GO) for dimension reduction of scRNA-seq data. By integrating GO with both unsupervised and supervised models, two novel methods are proposed, named GOAE (Gene Ontology AutoEncoder) and GONN (Gene Ontology Neural Network) respectively.
conclusionsThe evaluation results show that the proposed models outperform some state-of-the-art dimensionality reduction approaches. Furthermore, incorporating with GO, we provide an opportunity to interpret the underlying biological mechanism behind the neural network-based model.
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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.