Reinventing gene expression connectivity through regulatory and spatial structural empowerment via principal node aggregation graph neural network.
Nucleic Acids Res
; 52(13): e60, 2024 Jul 22.
Article
en En
| MEDLINE
| ID: mdl-38884259
ABSTRACT
The intricacies of the human genome, manifested as a complex network of genes, transcend conventional representations in text or numerical matrices. The intricate gene-to-gene relationships inherent in this complexity find a more suitable depiction in graph structures. In the pursuit of predicting gene expression, an endeavor shared by predecessors like the L1000 and Enformer methods, we introduce a novel spatial graph-neural network (GNN) approach. This innovative strategy incorporates graph features, encompassing both regulatory and structural elements. The regulatory elements include pair-wise gene correlation, biological pathways, protein-protein interaction networks, and transcription factor regulation. The spatial structural elements include chromosomal distance, histone modification and Hi-C inferred 3D genomic features. Principal Node Aggregation models, validated independently, emerge as frontrunners, demonstrating superior performance compared to traditional regression and other deep learning models. By embracing the spatial GNN paradigm, our method significantly advances the description of the intricate network of gene interactions, surpassing the performance, predictable scope, and initial requirements set by previous methods.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Redes Neurales de la Computación
/
Redes Reguladoras de Genes
Límite:
Humans
Idioma:
En
Revista:
Nucleic Acids Res
Año:
2024
Tipo del documento:
Article
País de afiliación:
Estados Unidos