Identifying key factors in cell fate decisions by machine learning interpretable strategies.
J Biol Phys
; 49(4): 443-462, 2023 12.
Article
em En
| MEDLINE
| ID: mdl-37458834
ABSTRACT
Cell fate decisions and transitions are common in almost all developmental processes. Therefore, it is important to identify the decision-making mechanisms and important individual molecules behind the fate decision processes. In this paper, we propose an interpretable strategy based on systematic perturbation, unsupervised hierarchical cluster analysis (HCA), machine learning (ML), and Shapley additive explanation (SHAP) analysis for inferring the contribution and importance of individual molecules in cell fate decision and transition processes. In order to verify feasibility of the approach, we apply it to the core epithelial to mesenchymal transition (EMT)-metastasis network. The key factors identified in EMT-metastasis are consistent with relevant experimental observations. The approach presented here can be applied to other biological networks to identify important factors related to cell fate decisions and transitions.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Transição Epitelial-Mesenquimal
/
Aprendizado de Máquina
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article