ArticleGenome biology2023
ENGEP: advancing spatial transcriptomics with accurate unmeasured gene expression prediction.
Article in Genome biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Gut microbiota alpha-diversity and taxonomic differences between maintenance hemodialysis patients and healthy controls: a systematic review and meta-analysis.International urology and nephrology · 2026Article
- Microenvironment-aware transcriptome reconstruction in spatial transcriptomics.Nature communications · 2026Article
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- R4ST: a reference-guided graph-generative model for robust reconstruction of spatial transcriptomic profiles.Bioinformatics (Oxford, England) · 2026Article
- Enhancing Spatial Transcriptomics via Spatially Constrained Matrix Decomposition with EDGES.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- DeepGFT: identifying spatial domains in spatial transcriptomics of complex and 3D tissue using deep learning and graph Fourier transform.Genome biology · 2025Article
- Analysis of research trends and hotspots in the primary treatment of end-stage renal disease.International urology and nephrology · 2025Review
- Untranslated region engineering strategies for gene overexpression, fine-tuning, and dynamic regulation.Journal of microbiology (Seoul, Korea) · 2025Review
- stAI: a deep learning-based model for missing gene imputation and cell-type annotation of spatial transcriptomics.Nucleic acids research · 2025Article
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Authors and funding
2 authors.
Funding
Abstract
Imaging-based spatial transcriptomics techniques provide valuable spatial and gene expression information at single-cell resolution. However, their current capability is restricted to profiling a limited number of genes per sample, resulting in most of the transcriptome remaining unmeasured. To overcome this challenge, we develop ENGEP, an ensemble learning-based tool that predicts unmeasured gene expression in spatial transcriptomics data by using multiple single-cell RNA sequencing datasets as references. ENGEP outperforms current state-of-the-art tools and brings biological insight by accurately predicting unmeasured genes. ENGEP has exceptional efficiency in terms of runtime and memory usage, making it scalable for analyzing large datasets.
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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.