Article

Continuous lifelong learning for modeling of gene regulation from single cell multiome data by leveraging atlas-scale external data

2023-08-03

Abstract excerpt

Accurate context-specific Gene Regulatory Networks (GRNs) inference from genomics data is a crucial task in computational biology. However, existing methods face limitations, such as reliance on gene expression data alone, lower resolution from bulk data, and data scarcity for specific cellular systems. Despite recent technological advancements, including single-cell sequencing and the integration of ATAC-seq and...

Topics

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

Identifiers and source

Literature Corpus work: ce4f80d3-1039-5052-923b-3263175ae756
DOI: 10.1101/2023.08.01.551575

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Continuous lifelong learning for modeling of gene regulation from single cell multiome data by leveraging atlas-scale external dataDOI 10.1101/2023.08.01.551575Read

Continuous lifelong learning for modeling of gene regulation from single cell multiome data by leveraging atlas-scale external dataSemantic proximity Read

scGRIP: a graph-based explainable AI framework for single-cell multi-omics Gene Regulatory Inference with Prior KnowledgeSemantic proximity Read

BayesCNet: Bayesian inference for cell type-specific regulatory networks leveraging cell type hierarchy in single-cell dataSemantic proximity Read

scRegulate: Single-Cell Regulatory-Embedded Variational Inference of Transcription Factor Activity from Gene ExpressionSemantic proximity Read

SCRIPT: predicting single-cell long-range <i>cis</i> -regulation based on pretrained graph attention networksSemantic proximity Read

Learning sequence-based regulatory dynamics in single-cell genomicsSemantic proximity Read

Context-aware sequence-to-function model of human gene regulationSemantic proximity Read

Graph attention network for link prediction of gene regulations from single-cell RNA-sequencing dataSemantic proximity Read

TRAPT: A multi-stage fused deep learning framework for transcriptional regulators prediction via integrating large-scale epigenomic dataSemantic proximity Read

Mapping enhancer-gene regulatory interactions from single-cell dataSemantic proximity Read

Population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomics dataSemantic proximity Read

scDirect: key transcription factor identification for directing cell state transitions based on single-cell multi-omics dataSemantic proximity Read