2021
DOI: 10.1101/2021.07.08.451697
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

scSGL: Signed Graph Learning for Single-Cell Gene Regulatory Network Inference

Abstract: Elucidating the topology of gene regulatory networks (GRN) from large single-cell RNA sequencing (scRNAseq) datasets, while effectively capturing its inherent cell-cycle heterogeneity, is currently one of the most pressing problems in computational systems biology. Recently, graph learning (GL) approaches based on graph signal processing (GSP) have been developed to infer graph topology from signals defined on graphs. However, existing GL methods are not suitable for learning signed graphs, which represent a c… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Year Published

2021
2021
2021
2021

Publication Types

Select...
1

Relationship

1
0

Authors

Journals

citations
Cited by 1 publication
references
References 81 publications
(129 reference statements)
0
0
0
Order By: Relevance