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.
|Number of pages||6|
|Journal||ESANN 2011 - 19th European Symposium on Artificial Neural Networks|
|Publication status||Published - 2011|