ArticleGenome medicine2022
Diagnostic Evidence GAuge of Single cells (DEGAS): a flexible deep transfer learning framework for prioritizing cells in relation to disease.
Article in Genome medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Cell-level random splits leak group-owned answers in single-cell benchmarks.bioRxiv : the preprint server for biology · 2026Article
- PhenoMapR: scalable mapping of sample phenotypes to single-cell, spatial, and bulk transcriptomics data.bioRxiv : the preprint server for biology · 2026Article
- scProtoTransformer: Scalable reference mapping across molecules, cells, and donors.Science advances · 2026Article
- Identification of high-risk cells in single-cell spatially resolved transcriptomics data using Diagnostic Evidence GAuge of Single-cells with spatial smoothing.Bioinformatics (Oxford, England) · 2026Article
- scSurvival: Single-Cell Survival Analysis of Clinical Cancer Cohort Data at Cellular Resolution.Cancer discovery · 2026Article
- TiRank prioritizes phenotypic niches in tumor microenvironment for clinical biomarker discovery.Genome medicine · 2026Article
- Single-cell analysis reveals neuroprotective histone deacetylase inhibitor pathways.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Identification and experimental validation of CCL26 as a core comorbid gene and biomarker for atopic dermatitis and allergic asthma via machine learning and multi-omics analysis.Frontiers in immunology · 2026Article
- Deep Transfer Learning Links Benign Glands to Prostate Cancer Progression via Transcriptomics.Genomics, proteomics & bioinformatics · 2025Article
- SIDISH integrates single-cell and bulk transcriptomics to identify high-risk cells and guide precision therapeutics through in silico perturbation.Nature communications · 2025Article
- Identification of type 2 diabetes- and obesity-associated human β-cells using deep transfer learning.eLife · 2025Article
- Graph-based deep learning for integrating single-cell and bulk transcriptomic data to identify clinical cancer subtypes.Briefings in bioinformatics · 2025Article
- SpaLinker identifies phenotype-associated spatial tumor microenvironment features by integrating bulk and spatial sequencing data.Cell genomics · 2025Article
- Uncovering latent biological function associations through gene set embeddings.BMC bioinformatics · 2025Article
- spatialGE Is a User-Friendly Web Application That Facilitates Spatial Transcriptomics Data Analysis.Cancer research · 2025Article
- Single-cell sequencing reveals the mechanisms of multiple myeloma progression: clarity or confusion?Annals of hematology · 2025Review
- scPAS: single-cell phenotype-associated subpopulation identifier.Briefings in bioinformatics · 2024Article
- Single-cell transcriptomes identify patient-tailored therapies for selective co-inhibition of cancer clones.Nature communications · 2024Article
- DrugReSC: targeting disease-critical cell subpopulations with single-cell transcriptomic data for drug repurposing in cancer.Briefings in bioinformatics · 2024Article
- scRank infers drug-responsive cell types from untreated scRNA-seq data using a target-perturbed gene regulatory network.Cell reports. Medicine · 2024Article
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
We propose DEGAS (Diagnostic Evidence GAuge of Single cells), a novel deep transfer learning framework, to transfer disease information from patients to cells. We call such transferrable information "impressions," which allow individual cells to be associated with disease attributes like diagnosis, prognosis, and response to therapy. Using simulated data and ten diverse single-cell and patient bulk tissue transcriptomic datasets from glioblastoma multiforme (GBM), Alzheimer's disease (AD), and multiple myeloma (MM), we demonstrate the feasibility, flexibility, and broad applications of the DEGAS framework. DEGAS analysis on myeloma single-cell transcriptomics identified PHF19
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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.