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SVM-based classification of class C GPCRs from alignment-free physicochemical transformations of their sequences

Author
König, C.; Cruz, R.; Alquezar, R.; Vellido, A.
Type of activity
Presentation of work at congresses
Name of edition
17th International Conference on Image Analysis and Processing
Date of publication
2013
Presentation's date
2013-09-09
Book of congress proceedings
New Trends in Image Analysis and Processing - ICIAP 2013
First page
336
Last page
343
Publisher
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/978-3-642-41190-8_36 Open in new window
Repository
http://hdl.handle.net/2117/23281 Open in new window
URL
http://link.springer.com/chapter/10.1007%2F978-3-642-41190-8_36 Open in new window
Abstract
G protein-coupled receptors (GPCRs) have a key function in regulating the function of cells due to their ability to transmit extracelullar signals. Given that the 3D structure and the functionality of most GPCRs is unknown, there is a need to construct robust classification models based on the analysis of their amino acid sequences for protein homology detection. In this paper, we describe the supervised classification of the different subtypes of class C GPCRs using support vector machines (SVM...
Citation
König, C. [et al.]. SVM-based classification of class C GPCRs from alignment-free physicochemical transformations of their sequences. A: International Conference on Image Analysis and Processing. "New Trends in Image Analysis and Processing - ICIAP 2013". Naples: Springer Berlin Heidelberg, 2013, p. 336-343.
Keywords
G-Protein coupled receptors, Homology, Pharmaco-proteomics, Supervised learning, Support vector machines, Transformation
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
SOCO - Soft Computing

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