Cargando…
Motif kernel generated by genetic programming improves remote homology and fold detection
BACKGROUND: Protein remote homology detection is a central problem in computational biology. Most recent methods train support vector machines to discriminate between related and unrelated sequences and these studies have introduced several types of kernels. One successful approach is to base a kern...
Autores principales: | Håndstad, Tony, Hestnes, Arne JH, Sætrom, Pål |
---|---|
Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2007
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1794419/ https://www.ncbi.nlm.nih.gov/pubmed/17254344 http://dx.doi.org/10.1186/1471-2105-8-23 |
Ejemplares similares
-
A ChIP-Seq Benchmark Shows That Sequence Conservation Mainly Improves Detection of Strong Transcription Factor Binding Sites
por: Håndstad, Tony, et al.
Publicado: (2011) -
Clustered ChIP-Seq-defined transcription factor binding sites and histone modifications map distinct classes of regulatory elements
por: Rye, Morten, et al.
Publicado: (2011) -
Cell-type specificity of ChIP-predicted transcription factor binding sites
por: Håndstad, Tony, et al.
Publicado: (2012) -
Building multiclass classifiers for remote homology detection and fold recognition
por: Rangwala, Huzefa, et al.
Publicado: (2006) -
Searching Remote Homology with Spectral Clustering with Symmetry in Neighborhood Cluster Kernels
por: Maulik, Ujjwal, et al.
Publicado: (2013)