Cargando…
Screening drug-target interactions with positive-unlabeled learning
Identifying drug-target interaction (DTI) candidates is crucial for drug repositioning. However, usually only positive DTIs are deposited in known databases, which challenges computational methods to predict novel DTIs due to the lack of negative samples. To overcome this dilemma, researchers usuall...
Autores principales: | Peng, Lihong, Zhu, Wen, Liao, Bo, Duan, Yu, Chen, Min, Chen, Yi, Yang, Jialiang |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5556112/ https://www.ncbi.nlm.nih.gov/pubmed/28808275 http://dx.doi.org/10.1038/s41598-017-08079-7 |
Ejemplares similares
-
Positive-Unlabeled Learning for inferring drug interactions based on heterogeneous attributes
por: Hameed, Pathima Nusrath, et al.
Publicado: (2017) -
DDI-PULearn: a positive-unlabeled learning method for large-scale prediction of drug-drug interactions
por: Zheng, Yi, et al.
Publicado: (2019) -
Positive-unlabeled learning for disease gene identification
por: Yang, Peng, et al.
Publicado: (2012) -
Positive-unlabelled learning of glycosylation sites in the human proteome
por: Li, Fuyi, et al.
Publicado: (2019) -
Ensemble Positive Unlabeled Learning for Disease Gene Identification
por: Yang, Peng, et al.
Publicado: (2014)