SPIN: sex-specific and pathway-based interpretable neural network for sexual dimorphism analysis.
Brief Bioinform
; 25(4)2024 May 23.
Article
in En
| MEDLINE
| ID: mdl-38807262
ABSTRACT
Sexual dimorphism in prevalence, severity and genetic susceptibility exists for most common diseases. However, most genetic and clinical outcome studies are designed in sex-combined framework considering sex as a covariate. Few sex-specific studies have analyzed males and females separately, which failed to identify gene-by-sex interaction. Here, we propose a novel unified biologically interpretable deep learning-based framework (named SPIN) for sexual dimorphism analysis. We demonstrate that SPIN significantly improved the C-index up to 23.6% in TCGA cancer datasets, and it was further validated using asthma datasets. In addition, SPIN identifies sex-specific and -shared risk loci that are often missed in previous sex-combined/-separate analysis. We also show that SPIN is interpretable for explaining how biological pathways contribute to sexual dimorphism and improve risk prediction in an individual level, which can result in the development of precision medicine tailored to a specific individual's characteristics.
Key words
Full text:
1
Database:
MEDLINE
Main subject:
Sex Characteristics
/
Neural Networks, Computer
Limits:
Female
/
Humans
/
Male
Language:
En
Journal:
Brief Bioinform
Journal subject:
BIOLOGIA
/
INFORMATICA MEDICA
Year:
2024
Type:
Article
Affiliation country:
United States