Cargando…
Gram-positive and gram-negative subcellular localization using rotation forest and physicochemical-based features
BACKGROUND: The functioning of a protein relies on its location in the cell. Therefore, predicting protein subcellular localization is an important step towards protein function prediction. Recent studies have shown that relying on Gene Ontology (GO) for feature extraction can improve the prediction...
Autores principales: | Dehzangi, Abdollah, Sohrabi, Sohrab, Heffernan, Rhys, Sharma, Alok, Lyons, James, Paliwal, Kuldip, Sattar, Abdul |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4347615/ https://www.ncbi.nlm.nih.gov/pubmed/25734546 http://dx.doi.org/10.1186/1471-2105-16-S4-S1 |
Ejemplares similares
-
Proposing a highly accurate protein structural class predictor using segmentation-based features
por: Dehzangi, Abdollah, et al.
Publicado: (2014) -
Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning
por: Heffernan, Rhys, et al.
Publicado: (2015) -
A strategy to select suitable physicochemical attributes of amino acids for protein fold recognition
por: Sharma, Alok, et al.
Publicado: (2013) -
Improving protein fold recognition using the amalgamation of evolutionary-based and structural based information
por: Paliwal, Kuldip K, et al.
Publicado: (2014) -
Multi-location gram-positive and gram-negative bacterial protein subcellular localization using gene ontology and multi-label classifier ensemble
por: Wang, Xiao, et al.
Publicado: (2015)