PheSeq, a Bayesian deep learning model to enhance and interpret the gene-disease association studies.
Genome Med
; 16(1): 56, 2024 04 16.
Article
en En
| MEDLINE
| ID: mdl-38627848
ABSTRACT
Despite the abundance of genotype-phenotype association studies, the resulting association outcomes often lack robustness and interpretations. To address these challenges, we introduce PheSeq, a Bayesian deep learning model that enhances and interprets association studies through the integration and perception of phenotype descriptions. By implementing the PheSeq model in three case studies on Alzheimer's disease, breast cancer, and lung cancer, we identify 1024 priority genes for Alzheimer's disease and 818 and 566 genes for breast cancer and lung cancer, respectively. Benefiting from data fusion, these findings represent moderate positive rates, high recall rates, and interpretation in gene-disease association studies.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Neoplasias de la Mama
/
Enfermedad de Alzheimer
/
Aprendizaje Profundo
/
Neoplasias Pulmonares
Límite:
Female
/
Humans
Idioma:
En
Revista:
Genome Med
Año:
2024
Tipo del documento:
Article
País de afiliación:
China
Pais de publicación:
Reino Unido