Cargando…
Gene ontology based transfer learning for protein subcellular localization
BACKGROUND: Prediction of protein subcellular localization generally involves many complex factors, and using only one or two aspects of data information may not tell the true story. For this reason, some recent predictive models are deliberately designed to integrate multiple heterogeneous data sou...
Autores principales: | Mei, Suyu, Fei, Wang, Zhou, Shuigeng |
---|---|
Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2011
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3039576/ https://www.ncbi.nlm.nih.gov/pubmed/21284890 http://dx.doi.org/10.1186/1471-2105-12-44 |
Ejemplares similares
-
Multi-Label Multi-Kernel Transfer Learning for Human Protein Subcellular Localization
por: Mei, Suyu
Publicado: (2012) -
mGOASVM: Multi-label protein subcellular localization based on gene ontology and support vector machines
por: Wan, Shibiao, et al.
Publicado: (2012) -
Correction: Multi-Label Multi-Kernel Transfer Learning for Human Protein Subcellular Localization
por: Mei, Suyu
Publicado: (2013) -
Correction: Multi-Label Multi-Kernel Transfer Learning for Human Protein Subcellular Localization
por: Mei, Suyu
Publicado: (2013) -
ProLoc-GO: Utilizing informative Gene Ontology terms for sequence-based prediction of protein subcellular localization
por: Huang, Wen-Lin, et al.
Publicado: (2008)