ArticleNucleic acids research2023
Gene knockout inference with variational graph autoencoder learning single-cell gene regulatory networks.
Article in Nucleic acids research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Graph neural networks for single-cell omics data: a review of approaches and applications.Briefings in bioinformatics · 2025Pooled it
- Identifying JAK2 and ANXA5 as Key Genes Linking Obstructive Sleep Apnea and Oxidative Stress via Machine Learning and Multilayer Transcriptomic Integration With Functional Validation.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026Article
- NEAT1 Coordinates a PDLIM5-CACNA1C Regulatory Program Associated with a Potentially Arrhythmogenic Cardiomyocyte State in the Border Zone During Early Myocardial Infarction.International journal of molecular sciences · 2026Article
- A transcription factor regulatory atlas for activity inference and perturbation prediction.Nucleic acids research · 2026Article
- scGPA: an LLM-assisted workflow for directional virtual gene perturbation analysis from single-cell transcriptomes.BMC genomics · 2026Article
- Machine learning-driven discovery of celastrol as an anti-inflammatory therapy suppressing NETs in severe influenza.Genes & diseases · 2026Article
- From undruggable to degradable: A deep learning-enabled framework for precision orthopaedic protein degradation.Journal of orthopaedic translation · 2026Review
- New approaches to discovering epigenetic rules of homeostasis in diverse mammal species.BMC genomics · 2026Article
- Ion-Channel-Mediated Drug Repurposing Opportunities Validated by Single-Cell Perturbation in Colorectal Cancer.International journal of molecular sciences · 2026Article
- TEDD 2.0: an advanced temporal gene expression database enabled by in-silico functional analyses for developmental mechanism investigation.Science China. Life sciences · 2026Article
- Genetic associations and candidate functional genes linking depression and obesity: a multi-omics integrative study.Frontiers in genetics · 2026Article
- Cell-GraphCompass: modeling single cells with graph structure foundation model.National science review · 2025Article
- scPOEM: robust co-embedding of peaks and genes revealing peak-gene regulation.Bioinformatics (Oxford, England) · 2025Article
- Dissecting crosstalk induced by cell-cell communication using single-cell transcriptomic data.Nature communications · 2025Article
- CoupleVAE: coupled variational autoencoders for predicting perturbational single-cell RNA sequencing data.Briefings in bioinformatics · 2025Article
- Gene function revealed at the moment of stochastic gene silencingCommunications biology · 2025Article
- Advances in modeling cellular state dynamics: integrating omics data and predictive techniques.Animal cells and systems · 2025Review
- A mini-review on perturbation modelling across single-cell omic modalities.Computational and structural biotechnology journal · 2024Review
- BioDSNN: a dual-stream neural network with hybrid biological knowledge integration for multi-gene perturbation response prediction.Briefings in bioinformatics · 2024Article
- Benchmarking clustering, alignment, and integration methods for spatial transcriptomics.Genome biology · 2024Article
Corrections and comments
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Authors and funding
8 authors.
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Abstract
In this paper, we introduce Gene Knockout Inference (GenKI), a virtual knockout (KO) tool for gene function prediction using single-cell RNA sequencing (scRNA-seq) data in the absence of KO samples when only wild-type (WT) samples are available. Without using any information from real KO samples, GenKI is designed to capture shifting patterns in gene regulation caused by the KO perturbation in an unsupervised manner and provide a robust and scalable framework for gene function studies. To achieve this goal, GenKI adapts a variational graph autoencoder (VGAE) model to learn latent representations of genes and interactions between genes from the input WT scRNA-seq data and a derived single-cell gene regulatory network (scGRN). The virtual KO data is then generated by computationally removing all edges of the KO gene-the gene to be knocked out for functional study-from the scGRN. The differences between WT and virtual KO data are discerned by using their corresponding latent parameters derived from the trained VGAE model. Our simulations show that GenKI accurately approximates the perturbation profiles upon gene KO and outperforms the state-of-the-art under a series of evaluation conditions. Using publicly available scRNA-seq data sets, we demonstrate that GenKI recapitulates discoveries of real-animal KO experiments and accurately predicts cell type-specific functions of KO genes. Thus, GenKI provides an in-silico alternative to KO experiments that may partially replace the need for genetically modified animals or other genetically perturbed systems.
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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.