Cargando…
eRFSVM: a hybrid classifier to predict enhancers-integrating random forests with support vector machines
BACKGROUND: Enhancers are tissue specific distal regulation elements, playing vital roles in gene regulation and expression. The prediction and identification of enhancers are important but challenging issues for bioinformatics studies. Existing computational methods, mostly single classifiers, can...
Autores principales: | Huang, Fang, Shen, Jiawei, Guo, Qingli, Shi, Yongyong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5226099/ https://www.ncbi.nlm.nih.gov/pubmed/28096768 http://dx.doi.org/10.1186/s41065-016-0012-2 |
Ejemplares similares
-
Support vector machines classifiers of physical activities in preschoolers
por: Zhao, Wei, et al.
Publicado: (2013) -
Classifying Dysphagic Swallowing Sounds with Support Vector Machines
por: Miyagi, Shigeyuki, et al.
Publicado: (2020) -
Hybrid Random Forest and Support Vector Machine Modeling for HVAC Fault Detection and Diagnosis
por: Tun, Wunna, et al.
Publicado: (2021) -
The Construction of Support Vector Machine Classifier Using the Firefly Algorithm
por: Chao, Chih-Feng, et al.
Publicado: (2015) -
Support vector machine classifier for prediction of the metastasis of colorectal cancer
por: Zhi, Jiajun, et al.
Publicado: (2018)