DNILMF-LDA: Prediction of lncRNA-Disease Associations by Dual-Network Integrated Logistic Matrix Factorization and Bayesian Optimization

Identifying associations between lncRNAs and diseases can help understand disease-related lncRNAs and facilitate disease diagnosis and treatment. The dual-network integrated logistic matrix factorization (DNILMF) model has been used for drug–target interaction prediction, and good results have been...

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Detalles Bibliográficos
Autores principales: Li, Yan, Li, Junyi, Bian, Naizheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6722840/
https://www.ncbi.nlm.nih.gov/pubmed/31409034
http://dx.doi.org/10.3390/genes10080608

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6722840/
https://www.ncbi.nlm.nih.gov/pubmed/31409034
http://dx.doi.org/10.3390/genes10080608

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