Article

mcBERT: Patient-Level Single-cell Transcriptomics Data Representation

2024-11-06

Abstract excerpt

Single-cell RNA sequencing (scRNA-seq) transcriptomics improves our understanding of cellular heterogeneity in healthy and pathological states. However, most scRNA-seq analyses remain confined to single cells or distinct cell populations, limiting their clinical applicability. Addressing the need to translate single-cell insights into a patient-level disease understanding, we introduce mcBERT, a new method that le...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 9c147a80-ecbb-560c-a1e2-d15759ce8c98
DOI: 10.1101/2024.11.04.621897

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

mcBERT: Patient-Level Single-cell Transcriptomics Data RepresentationDOI 10.1101/2024.11.04.621897Read

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

A Diffusion-Based Autoencoder for Learning Patient-Level Representations from Single-Cell DataSemantic proximity Read

scTransMIL Bridges Patient-Level Phenotypes and Single-Cell Transcriptomics for Cancer Screening and Heterogeneity InferenceSemantic proximity Read

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

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

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

Inferring Disease Progressive Stages in Single-Cell Transcriptomics Using Weakly-Supervised Deep Learning ApproachSemantic proximity Read

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

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

GeneBag: training a cell foundation model for broad-spectrum cancer diagnosis and prognosis with bulk RNA-seq dataSemantic proximity Read

Discovering cell types underlying rare disease phenotypes using scRNA-seq data from non-diseased tissuesSemantic proximity Read

Cell type composition drives patient stratification in single-cell RNA-seq cohortsSemantic proximity Read