SSLpheno: a self-supervised learning approach for gene–phenotype association prediction using protein–protein interactions and gene ontology data

MOTIVATION: Medical genomics faces significant challenges in interpreting disease phenotype and genetic heterogeneity. Despite the establishment of standardized disease phenotype databases, computational methods for predicting gene–phenotype associations still suffer from imbalanced category distrib...

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Detalles Bibliográficos
Autores principales: Bi, Xuehua, Liang, Weiyang, Zhao, Qichang, Wang, Jianxin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Original Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10666204/
https://www.ncbi.nlm.nih.gov/pubmed/37941450
http://dx.doi.org/10.1093/bioinformatics/btad662

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10666204/
https://www.ncbi.nlm.nih.gov/pubmed/37941450
http://dx.doi.org/10.1093/bioinformatics/btad662

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