Cargando…
Accurate splice site prediction using support vector machines
BACKGROUND: For splice site recognition, one has to solve two classification problems: discriminating true from decoy splice sites for both acceptor and donor sites. Gene finding systems typically rely on Markov Chains to solve these tasks. RESULTS: In this work we consider Support Vector Machines f...
Autores principales: | Sonnenburg, Sören, Schweikert, Gabriele, Philips, Petra, Behr, Jonas, Rätsch, Gunnar |
---|---|
Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2007
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2230508/ https://www.ncbi.nlm.nih.gov/pubmed/18269701 http://dx.doi.org/10.1186/1471-2105-8-S10-S7 |
Ejemplares similares
-
POIMs: positional oligomer importance matrices—understanding support vector machine-based signal detectors
por: Sonnenburg, Sören, et al.
Publicado: (2008) -
mGene.web: a web service for accurate computational gene finding
por: Schweikert, Gabriele, et al.
Publicado: (2009) -
Learning Interpretable SVMs for Biological Sequence Classification
por: Rätsch, Gunnar, et al.
Publicado: (2006) -
Support Vector Machines and Kernels for Computational Biology
por: Ben-Hur, Asa, et al.
Publicado: (2008) -
Support Vector Machine Implementations for Classification & Clustering
por: Winters-Hilt, Stephen, et al.
Publicado: (2006)