MHGTMDA: Molecular heterogeneous graph transformer based on biological entity graph for miRNA-disease associations prediction
MicroRNAs (miRNAs) play a crucial role in the prevention, prognosis, diagnosis, and treatment of complex diseases. Existing computational methods primarily focus on biologically relevant molecules directly associated with miRNA or disease, overlooking the fact that the human body is a highly complex...
Saved in:
Main Authors: | Haitao Zou (Author), Boya Ji (Author), Meng Zhang (Author), Fen Liu (Author), Xiaolan Xie (Author), Shaoliang Peng (Author) |
---|---|
Format: | Book |
Published: |
Elsevier,
2024-03-01T00:00:00Z.
|
Subjects: | |
Online Access: | Connect to this object online. |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
MiRNA-Drug Resistance Association Prediction Through the Attentive Multimodal Graph Convolutional Network
by: Yanqing Niu, et al.
Published: (2022) -
Novel link prediction for large-scale miRNA-lncRNA interaction network in a bipartite graph
by: Zhi-An Huang, et al.
Published: (2018) -
Inferring miRNA-disease associations using collaborative filtering and resource allocation on a tripartite graph
by: Van Tinh Nguyen, et al.
Published: (2021) -
Heterogeneous graph construction and node representation learning method of Treatise on Febrile Diseases based on graph convolutional network
by: Junfeng YAN, et al.
Published: (2022) -
Combining non-negative matrix factorization with graph Laplacian regularization for predicting drug-miRNA associations based on multi-source information fusion
by: Mei-Neng Wang, et al.
Published: (2023)