Cargando…
MLNGCF: circRNA–disease associations prediction with multilayer attention neural graph-based collaborative filtering
MOTIVATION: CircRNAs play a critical regulatory role in physiological processes, and the abnormal expression of circRNAs can mediate the processes of diseases. Therefore, exploring circRNAs–disease associations is gradually becoming an important area of research. Due to the high cost of validating c...
Autores principales: | Wu, Qunzhuo, Deng, Zhaohong, Zhang, Wei, Pan, Xiaoyong, Choi, Kup-Sze, Zuo, Yun, Shen, Hong-Bin, Yu, Dong-Jun |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10457666/ https://www.ncbi.nlm.nih.gov/pubmed/37561093 http://dx.doi.org/10.1093/bioinformatics/btad499 |
Ejemplares similares
-
Predicting circRNA-drug sensitivity associations via graph attention auto-encoder
por: Deng, Lei, et al.
Publicado: (2022) -
GATCDA: Predicting circRNA-Disease Associations Based on Graph Attention Network
por: Bian, Chen, et al.
Publicado: (2021) -
MIGAN: Mutual-Interaction Graph Attention Network for Collaborative Filtering
por: Drif, Ahlem, et al.
Publicado: (2022) -
GATNNCDA: A Method Based on Graph Attention Network and Multi-Layer Neural Network for Predicting circRNA-Disease Associations
por: Ji, Cunmei, et al.
Publicado: (2021) -
Prediction of circRNA-Disease Associations Based on the Combination of Multi-Head Graph Attention Network and Graph Convolutional Network
por: Cao, Ruifen, et al.
Publicado: (2022)