A probabilistic approach to the visual exploration of G Protein-Coupled Receptor sequences
- Resource Type
- Authors
- Vellido, A.; Cárdenas, M. I.; Olier, I.; Xavier Rovira; Giraldo, J.
- Source
- Scopus-Elsevier
UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
Recercat. Dipósit de la Recerca de Catalunya
instname
- Subject
- Informàtica::Intel·ligència artificial::Aprenentatge automàtic [Àrees temàtiques de la UPC]
Machine learning
Aprenentatge automàtic
Proteïnes G
Proteïnes -- Investigació
Protein research
G proteins
- Language
The study of G protein-coupled receptors (GPCRs) is of great interest in pharmaceutical research, but only a few of their 3D structures are known at present. On the contrary, their amino acid sequences are known and accessible. Sequence analysis can provide new insight on GPCR function. Here, we use a kernel-based statistical machine learning model for the visual exploration of GPCR functional groups from their sequences. This is based on the rich information provided by the model regarding the probability of each sequence belonging to a certain receptor group.