ArticleGenome biology2020
DISC: a highly scalable and accurate inference of gene expression and structure for single-cell transcriptomes using semi-supervised deep learning.
Article in Genome biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Prior-guided factorization for reliable imputation of scRNA-seq data.PLoS computational biology · 2026Article
- Imputing single-cell protein abundance in multiplex tissue imaging.Nature communications · 2025Article
- Scmaskgan: masked multi-scale CNN and attention-enhanced GAN for scRNA-seq dropout imputation.BMC bioinformatics · 2025Article
- Artificial intelligence and machine learning applications for cultured meat.Frontiers in artificial intelligence · 2024Review
- Exploring Dysregulated Ferroptosis-Related Genes in Septic Myocardial Injury Based on Human Heart Transcriptomes: Evidence and New Insights.Journal of inflammation research · 2023Article
- Trends and Potential of Machine Learning and Deep Learning in Drug Study at Single-Cell Level.Research (Washington, D.C.) · 2023Review
- Review
- CaSee: A lightning transfer-learning model directly used to discriminate cancer/normal cells from scRNA-seq.Oncogene · 2022Article
- scMTD: a statistical multidimensional imputation method for single-cell RNA-seq data leveraging transcriptome dynamic information.Cell & bioscience · 2022Article
- Review
- scIMC: a platform for benchmarking comparison and visualization analysis of scRNA-seq data imputation methods.Nucleic acids research · 2022Article
- Statistics or biology: the zero-inflation controversy about scRNA-seq data.Genome biology · 2022Review
- Knowledge structure and emerging trends in the application of deep learning in genetics research: A bibliometric analysis [2000-2021].Frontiers in genetics · 2022Article
- Bibliometric analysis of artificial intelligence for biotechnology and applied microbiology: Exploring research hotspots and frontiers.Frontiers in bioengineering and biotechnology · 2022Review
- Virtual Gene Concept and a Corresponding Pragmatic Research Program in Genetical Data Science.Entropy (Basel, Switzerland) · 2021Review
- Single-Cell Techniques and Deep Learning in Predicting Drug Response.Trends in pharmacological sciences · 2020Review
- DISC: a highly scalable and accurate inference of gene expression and structure for single-cell transcriptomes using semi-supervised deep learning.Genome biology · 2020Article
- Artificial Intelligence Models for Cell Type and Subtype Identification Based on Single-Cell RNA Sequencing Data in Vision Science.IEEE/ACM transactions on computational biology and bioinformaticsReview
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dropouts distort gene expression and misclassify cell types in single-cell transcriptome. Although imputation may improve gene expression and downstream analysis to some degree, it also inevitably introduces false signals. We develop DISC, a novel deep learning network with semi-supervised learning to infer gene structure and expression obscured by dropouts. Compared with seven state-of-the-art imputation approaches on ten real-world datasets, we show that DISC consistently outperforms the other approaches. Its applicability, scalability, and reliability make DISC a promising approach to recover gene expression, enhance gene and cell structures, and improve cell type identification for sparse 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.