Cargando…
Prediction of Transmembrane Proteins from Their Primary Sequence by Support Vector Machine Approach
Prediction of transmembrane (TM) proteins from their sequence facilitates functional study of genomes and the search of potential membrane-associated therapeutic targets. Computational methods for predicting TM sequences have been developed. These methods achieve high prediction accuracy for many TM...
Autores principales: | Cai, C. Z., Yuan, Q. F., Xiao, H. G., Liu, X. H., Han, L. Y., Chen, Y. Z. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2006
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7121931/ http://dx.doi.org/10.1007/11816102_56 |
Ejemplares similares
-
Transmembrane protein topology prediction using support vector machines
por: Nugent, Timothy, et al.
Publicado: (2009) -
Epithelial–mesenchymal transition biomarkers and support vector machine guided model in preoperatively predicting regional lymph node metastasis for rectal cancer
por: Fan, X-J, et al.
Publicado: (2012) -
Prediction of Candidate Primary Immunodeficiency Disease Genes Using a Support Vector Machine Learning Approach
por: Keerthikumar, Shivakumar, et al.
Publicado: (2009) -
Predicting primary progressive aphasias with support vector machine approaches in structural MRI data
por: Bisenius, Sandrine, et al.
Publicado: (2017) -
Support vector machine-based nomogram predicts postoperative distant metastasis for patients with oesophageal squamous cell carcinoma
por: Yang, H X, et al.
Publicado: (2013)