Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Exploring phenotype-related single-cells through attention-enhanced representation learning.

Genome medicine - 21 Jan 2026

Wu Qinhua, Ding Junxiang, He Ruikun, Hui Lijian, Liu Junwei, Li Yixue

Abstract excerpt

BACKGROUND: Atlas-level single-cell investigations elucidate disease pathogenesis and progression. Accurate interpretation of phenotype-related single-cell data necessitates pre-defining cell subtypes and identifying their abundance variations. However, batch correction and clustering resolution biases can impact this interpretation. To overcome these challenges, an end-to-end integrative approach that combines...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Aging
Share to COVID-19
Share to Deep Learning

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Leukocytes, Mononuclear

Share to Phenotype

Share to Single-Cell Analysis

Share to Single-Cell Gene Expression Analysis

Source

Open in PubMed Open full text

Identifiers

PMID: 41566378
DOI: 10.1186/s13073-026-01598-x
PMCID: PMC12906058

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Exploring phenotype-related single-cells through attention-enhanced representation learning.PMID 41566378Read ↗

Exploring phenotype-related single-cells through attention-enhanced representation learningSemantic proximity Read ↗

Weakly supervised learning uncovers phenotypic signatures in single-cell dataSemantic proximity Read ↗

Learning multi-cellular representations of single-cell transcriptomics data enables characterization of patient-level disease statesSemantic proximity Read ↗

mcBERT: Patient-Level Single-cell Transcriptomics Data RepresentationSemantic proximity Read ↗

Phenotype prediction from single-cell RNA-seq data using attention-based neural networks.Semantic proximity Read ↗

Clinical Phenotype Prediction From Single-cell RNA-seq Data using Attention-Based Neural NetworksSemantic proximity Read ↗

Annotation-free phenotype prediction using knowledge-augmented clustering from single-cell RNA sequencing data.Semantic proximity Read ↗

Deep interpretable learning of sample representations for characterizing disease states in single-cell transcriptomicsSemantic proximity Read ↗

Incorporating Hierarchical Information into Multiple Instance Learning for Patient Phenotype Prediction with scRNA-seq DataSemantic proximity Read ↗

scMILD: Single-cell Multiple Instance Learning for Sample Classification and Associated Subpopulation DiscoverySemantic proximity Read ↗

Self-distillation contrastive learning enables clustering-free signature extraction and mapping to multimodal single-cell atlas of multimillion scaleSemantic proximity Read ↗

Population-level integration of single-cell datasets enables multi-scale analysis across samplesSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.