Structure learning for gene regulatory networks.
PLoS Comput Biol
; 19(5): e1011118, 2023 05.
Article
in En
| MEDLINE
| ID: mdl-37200395
ABSTRACT
Inference of biological network structures is often performed on high-dimensional data, yet is hindered by the limited sample size of high throughput "omics" data typically available. To overcome this challenge, often referred to as the "small n, large p problem," we exploit known organizing principles of biological networks that are sparse, modular, and likely share a large portion of their underlying architecture. We present SHINE-Structure Learning for Hierarchical Networks-a framework for defining data-driven structural constraints and incorporating a shared learning paradigm for efficiently learning multiple Markov networks from high-dimensional data at large p/n ratios not previously feasible. We evaluated SHINE on Pan-Cancer data comprising 23 tumor types, and found that learned tumor-specific networks exhibit expected graph properties of real biological networks, recapture previously validated interactions, and recapitulate findings in literature. Application of SHINE to the analysis of subtype-specific breast cancer networks identified key genes and biological processes for tumor maintenance and survival as well as potential therapeutic targets for modulating known breast cancer disease genes.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Breast Neoplasms
/
Gene Regulatory Networks
Type of study:
Prognostic_studies
Limits:
Female
/
Humans
Language:
En
Journal:
PLoS Comput Biol
Journal subject:
BIOLOGIA
/
INFORMATICA MEDICA
Year:
2023
Type:
Article
Affiliation country:
United States