Cargando…
Combining classifiers for improved classification of proteins from sequence or structure
BACKGROUND: Predicting a protein's structural or functional class from its amino acid sequence or structure is a fundamental problem in computational biology. Recently, there has been considerable interest in using discriminative learning algorithms, in particular support vector machines (SVMs)...
Autores principales: | Melvin, Iain, Weston, Jason, Leslie, Christina S, Noble, William S |
---|---|
Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2008
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2561051/ https://www.ncbi.nlm.nih.gov/pubmed/18808707 http://dx.doi.org/10.1186/1471-2105-9-389 |
Ejemplares similares
-
Rankprop: a web server for protein remote homology detection
por: Melvin, Iain, et al.
Publicado: (2009) -
Detecting Remote Evolutionary Relationships among Proteins by Large-Scale Semantic Embedding
por: Melvin, Iain, et al.
Publicado: (2011) -
SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition
por: Melvin, Iain, et al.
Publicado: (2007) -
Protein Ranking by Semi-Supervised Network Propagation
por: Weston, Jason, et al.
Publicado: (2006) -
Improved Bevirimat resistance prediction by combination of structural and sequence-based classifiers
por: Dybowski, J Nikolaj, et al.
Publicado: (2011)