Cargando…
Wrapper-based selection of genetic features in genome-wide association studies through fast matrix operations
BACKGROUND: Through the wealth of information contained within them, genome-wide association studies (GWAS) have the potential to provide researchers with a systematic means of associating genetic variants with a wide variety of disease phenotypes. Due to the limitations of approaches that have anal...
Autores principales: | Pahikkala, Tapio, Okser, Sebastian, Airola, Antti, Salakoski, Tapio, Aittokallio, Tero |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2012
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3606421/ https://www.ncbi.nlm.nih.gov/pubmed/22551170 http://dx.doi.org/10.1186/1748-7188-7-11 |
Ejemplares similares
-
Regularized Machine Learning in the Genetic Prediction of Complex Traits
por: Okser, Sebastian, et al.
Publicado: (2014) -
Genetic variants and their interactions in disease risk prediction – machine learning and network perspectives
por: Okser, Sebastian, et al.
Publicado: (2013) -
All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning
por: Airola, Antti, et al.
Publicado: (2008) -
Toward more realistic drug–target interaction predictions
por: Pahikkala, Tapio, et al.
Publicado: (2015) -
Learning with multiple pairwise kernels for drug bioactivity prediction
por: Cichonska, Anna, et al.
Publicado: (2018)