Precious1GPT: multimodal transformer-based transfer learning for aging clock development and feature importance analysis for aging and age-related disease target discovery.
Aging (Albany NY)
; 15(11): 4649-4666, 2023 06 13.
Article
em En
| MEDLINE
| ID: mdl-37315204
ABSTRACT
Aging is a complex and multifactorial process that increases the risk of various age-related diseases and there are many aging clocks that can accurately predict chronological age, mortality, and health status. These clocks are disconnected and are rarely fit for therapeutic target discovery. In this study, we propose a novel approach to multimodal aging clock we call Precious1GPT utilizing methylation and transcriptomic data for interpretable age prediction and target discovery developed using a transformer-based model and transfer learning for case-control classification. While the accuracy of the multimodal transformer is lower within each individual data type compared to the state of art specialized aging clocks based on methylation or transcriptomic data separately it may have higher practical utility for target discovery. This method provides the ability to discover novel therapeutic targets that hypothetically may be able to reverse or accelerate biological age providing a pathway for therapeutic drug discovery and validation using the aging clock. In addition, we provide a list of promising targets annotated using the PandaOmics industrial target discovery platform.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Perfilação da Expressão Gênica
/
Aprendizado de Máquina
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
Aging (Albany NY)
Assunto da revista:
GERIATRIA
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Hong Kong