Cargando…
CERENKOV3: Clustering and molecular network-derived features improve computational prediction of functional noncoding SNPs
Identification of causal noncoding single nucleotide polymorphisms (SNPs) is important for maximizing the knowledge dividend from human genome-wide association studies (GWAS). Recently, diverse machine learning-based methods have been used for functional SNP identification; however, this task remain...
Autores principales: | Yao, Yao, Ramsey, Stephen A. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6897322/ https://www.ncbi.nlm.nih.gov/pubmed/31797625 |
Ejemplares similares
-
CERENKOV2: improved detection of functional noncoding SNPs using data-space geometric features
por: Yao, Yao, et al.
Publicado: (2019) -
Cerenkov detectors
por: Meunier, R
Publicado: (1973) -
Cerenkov counters
por: Marshall, J
Publicado: (1956) -
Cerenkov counters
por: Hofstadter, R
Publicado: (1956) -
Multiomics dissection of molecular regulatory mechanisms underlying autoimmune-associated noncoding SNPs
por: Chen, Xiao-Feng, et al.
Publicado: (2020)