Overview of the TransMarker framework for identifying DNBs from single cell time course data.
<p>(A) State specific gene expression profiles are integrated with a prior regulatory network to construct attributed gene interaction graphs. (B) A set of rewired graphs is generated to represent disease progression across multiple states. (C) Topological Feature Integration incorporates both...
Enregistré dans:
| Auteur principal: | |
|---|---|
| Autres auteurs: | , , |
| Publié: |
2025
|
| Sujets: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| Résumé: | <p>(A) State specific gene expression profiles are integrated with a prior regulatory network to construct attributed gene interaction graphs. (B) A set of rewired graphs is generated to represent disease progression across multiple states. (C) Topological Feature Integration incorporates both local and global structural properties into each graph. (D) GATs are applied to learn contextualized embeddings for each disease state. (E) Gromov-Wasserstein optimal transport computes alignment scores between state specific networks to identify genes with high structural shifts. (F) A union subnetwork of selected genes is used to compute a DNI, identifying DNBs based on network variability. (G) The resulting DNBs and initial feature matrices are input to a DNN for multiclass classification across disease states.</p> |
|---|