Ensemble-GNN: federated ensemble learning with graph neural networks for disease module discovery and classification.
Bioinformatics
; 39(11)2023 11 01.
Article
em En
| MEDLINE
| ID: mdl-37988152
ABSTRACT
SUMMARY:
Federated learning enables collaboration in medicine, where data is scattered across multiple centers without the need to aggregate the data in a central cloud. While, in general, machine learning models can be applied to a wide range of data types, graph neural networks (GNNs) are particularly developed for graphs, which are very common in the biomedical domain. For instance, a patient can be represented by a protein-protein interaction (PPI) network where the nodes contain the patient-specific omics features. Here, we present our Ensemble-GNN software package, which can be used to deploy federated, ensemble-based GNNs in Python. Ensemble-GNN allows to quickly build predictive models utilizing PPI networks consisting of various node features such as gene expression and/or DNA methylation. We exemplary show the results from a public dataset of 981 patients and 8469 genes from the Cancer Genome Atlas (TCGA). AVAILABILITY AND IMPLEMENTATION The source code is available at https//github.com/pievos101/Ensemble-GNN, and the data at Zenodo (DOI 10.5281/zenodo.8305122).
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Metilação de DNA
/
Aprendizado de Máquina
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article