Article

CellTypeAI: Automated cell identification for scRNA-seq using local generative-AI

2026-03-05

Abstract excerpt

Single-cell RNA sequencing (scRNA-seq) cell annotation techniques rely on the matching of known defining marker genes to a given cell population. However, these methods may lack robustness to dynamic fluctuations in cell marker expression between patients, samples and pathologies. The advent of easy-to-implement predictive technologies, like generative-AI (gen-AI), has facilitated the introduction of computational...

Topics

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

Identifiers and source

Literature Corpus work: c242fb5f-f7df-5ec2-a5ef-ca93533a1485
DOI: 10.64898/2026.03.03.709253

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

CellTypeAI: Automated cell identification for scRNA-seq using local generative-AIDOI 10.64898/2026.03.03.709253Read

Generative single-cell transcriptomics via large language modelsSemantic proximity Read

CellDiffusion: a generative model to annotate single-cell and spatial RNA-seq using bulk referencesSemantic proximity Read

Evaluation of Generative AI Models for Processing Single-Cell RNA-Sequencing Data in Human Pancreatic TissueSemantic proximity Read

scGenAI: A generative AI platform with biological context embedding of multimodal features enhances single cell state classificationSemantic proximity Read

Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic dataSemantic proximity Read

CellMeSH: Probabilistic Cell-Type Identification Using Indexed LiteratureSemantic proximity Read

MACA: Marker-based automatic cell-type annotation for single cell expression dataSemantic proximity Read

<i>ACTIVA</i> : realistic single-cell RNA-seq generation with automatic cell-type identification using introspective variational autoencodersSemantic proximity Read

Prototype-based continual cell-type annotation reveals cellular state transitions in expanding single-cell atlasesSemantic proximity Read

Benchmarking Machine Learning Models for Cell Type Annotation in Single-Cell vs Single-Nucleus RNA-Seq DataSemantic proximity Read

HitAnno: Atlas-level cell type annotation based on scATAC-seq data via a hierarchical language modelSemantic proximity Read

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