Cargando…
Selecting high-quality negative samples for effectively predicting protein-RNA interactions
BACKGROUND: The identification of Protein-RNA Interactions (PRIs) is important to understanding cell activities. Recently, several machine learning-based methods have been developed for identifying PRIs. However, the performance of these methods is unsatisfactory. One major reason is that they usual...
Autores principales: | Cheng, Zhanzhan, Huang, Kai, Wang, Yang, Liu, Hui, Guan, Jihong, Zhou, Shuigeng |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5374704/ https://www.ncbi.nlm.nih.gov/pubmed/28361676 http://dx.doi.org/10.1186/s12918-017-0390-8 |
Ejemplares similares
-
Improving compound–protein interaction prediction by building up highly credible negative samples
por: Liu, Hui, et al.
Publicado: (2015) -
Molecular property prediction by contrastive learning with attention-guided positive sample selection
por: Wang, Jinxian, et al.
Publicado: (2023) -
Fusing multiple protein-protein similarity networks to effectively predict lncRNA-protein interactions
por: Zheng, Xiaoxiong, et al.
Publicado: (2017) -
Protein function prediction by collective classification with explicit and implicit edges in protein-protein interaction networks
por: Xiong, Wei, et al.
Publicado: (2013) -
Exploiting topic modeling to boost metagenomic reads binning
por: Zhang, Ruichang, et al.
Publicado: (2015)