A probabilistic approach to the visual exploration of G Protein-Coupled Receptor sequences
作者： Iván Olier Caparroso , Jesús Giraldo , Martha Ivón Cárdenas , Xavier Rovira , Alfredo Vellido Alcacena
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摘要: The study of G protein-coupled receptors (GPCRs) is great interest in pharmaceutical research, but only a few their 3D structures are known at present. On the contrary, amino acid sequences and accessible. Sequence analysis can provide new insight on GPCR function. Here, we use kernel-based statistical machine learning model for visual exploration functional groups from sequences. This based rich information provided by regarding probability each sequence belonging to certain receptor group.
参考文章(6)
Sean R Eddy, None, Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids ,(1998)
M. C. Cobanoglu, Y. Saygin, U. Sezerman, Classification of GPCRs Using Family Specific Motifs IEEE/ACM Transactions on Computational Biology and Bioinformatics. ,vol. 8, pp. 1495- 1508 ,(2011) , 10.1109/TCBB.2010.101
Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams, GTM: the generative topographic mapping Neural Computation. ,vol. 10, pp. 215- 234 ,(1998) , 10.1162/089976698300017953
Fabrice Rossi, Nathalie Villa, Domaine de Voluceau, A comparison between dissimilarity SOM and kernel SOM for clustering the vertices of a graph workshop on self organizing maps. ,(2007)
Alfredo Vellido, Iván Olier, Jesús Giraldo, Kernel Generative Topographic Mapping the european symposium on artificial neural networks. ,(2010)
Bernhard Schölkopf, Learning with kernels ,(2001)