Article

scEMB: Learning context representation of genes based on large-scale single-cell transcriptomics

2024-09-26

Abstract excerpt

<h4>ABSTRACT</h4> <h4>Background</h4> The rapid advancement of single-cell transcriptomic technologies has led to the curation of millions of cellular profiles, providing unprecedented insights into cellular heterogeneity across various tissues and developmental stages. This growing wealth of data presents an opportunity to uncover complex gene-gene relationships, yet also poses significant computational challen...

Topics

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

Identifiers and source

Literature Corpus work: 3228572d-8c39-53e1-b5e5-4117930c3045
DOI: 10.1101/2024.09.24.614685

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

scEMB: Learning context representation of genes based on large-scale single-cell transcriptomicsDOI 10.1101/2024.09.24.614685Read

RegFormer: A Single-Cell Foundation Model Powered by Gene Regulatory HierarchiesSemantic proximity Read

scLong: A Billion-Parameter Foundation Model for Capturing Long-Range Gene Context in Single-Cell TranscriptomicsSemantic proximity Read

scLong: A Billion-Parameter Foundation Model for Capturing Long-Range Gene Context 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

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

Quantized multi-task learning for context-specific representations of gene network dynamicsSemantic proximity Read

stFormer: a foundation model for spatial transcriptomicsSemantic proximity Read

Scaling Dense Representations for Single Cell with Transcriptome-Scale ContextSemantic proximity Read

GatorSC: Multi-Scale Cell and Gene Graphs with Mixture-of-Experts Fusion for Single-Cell TranscriptomicsSemantic proximity Read

ChromBERT-tools: A versatile toolkit for context-specific embedding of transcription regulators across different cell typesSemantic proximity Read

scKEPLM: Knowledge enhanced large-scale pre-trained language model for single-cell transcriptomicsSemantic proximity Read

Parameter-free representations outperform single-cell foundation models on downstream benchmarksSemantic proximity Read