Cargando…
Improving the Caenorhabditis elegans Genome Annotation Using Machine Learning
For modern biology, precise genome annotations are of prime importance, as they allow the accurate definition of genic regions. We employ state-of-the-art machine learning methods to assay and improve the accuracy of the genome annotation of the nematode Caenorhabditis elegans. The proposed machine...
Autores principales: | Rätsch, Gunnar, Sonnenburg, Sören, Srinivasan, Jagan, Witte, Hanh, Müller, Klaus-R, Sommer, Ralf-J, Schölkopf, Bernhard |
---|---|
Formato: | Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2007
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1808025/ https://www.ncbi.nlm.nih.gov/pubmed/17319737 http://dx.doi.org/10.1371/journal.pcbi.0030020 |
Ejemplares similares
-
Support Vector Machines and Kernels for Computational Biology
por: Ben-Hur, Asa, et al.
Publicado: (2008) -
Learning Interpretable SVMs for Biological Sequence Classification
por: Rätsch, Gunnar, et al.
Publicado: (2006) -
POIMs: positional oligomer importance matrices—understanding support vector machine-based signal detectors
por: Sonnenburg, Sören, et al.
Publicado: (2008) -
Accurate splice site prediction using support vector machines
por: Sonnenburg, Sören, et al.
Publicado: (2007) -
Application of ALFA-Tagging in the Nematode Model Organisms Caenorhabditis elegans and Pristionchus pacificus
por: Igreja, Catia, et al.
Publicado: (2022)