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Kernel Generative Topographic Mapping of Protein Sequences
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Author(s): M.I. Cardenas (Universitat Politècnica de Catalunya, Spain), A. Vellido (Universitat Politècnica de Catalunya, Spain), I. Olier (The University of Manchester, UK), X. Rovira (Institut de Neurociències, Universitat Autònoma de Barcelona, Spain)and J. Giraldo (Institut de Neurociències, Universitat Autònoma de Barcelona, Spain)
Copyright: 2012
Pages: 14
Source title:
Medical Applications of Intelligent Data Analysis: Research Advancements
Source Author(s)/Editor(s): Rafael Magdalena-Benedito (Intelligent Data Analysis Laboratory, University of Valencia, Spain), Emilio Soria-Olivas (Intelligent Data Analysis Laboratory, University of Valencia, Spain), Juan Guerrero Martínez (Intelligent Data Analysis Laboratory, University of Valencia, Spain), Juan Gómez-Sanchis (Intelligent Data Analysis Laboratory, University of Valencia, Spain)and Antonio Jose Serrano-López (Intelligent Data Analysis Laboratory, University of Valencia, Spain)
DOI: 10.4018/978-1-4666-1803-9.ch013
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Abstract
The world of pharmacology is becoming increasingly dependent on the advances in the fields of genomics and proteomics. The –omics sciences bring about the challenge of how to deal with the large amounts of complex data they generate from an intelligence data analysis perspective. In this chapter, the authors focus on the analysis of a specific type of proteins, the G protein-couple receptors, which are the target for over 15% of current drugs. They describe a kernel method of the manifold learning family for the analysis of protein amino acid symbolic sequences. This method sheds light on the structure of protein subfamilies, while providing an intuitive visualization of such structure.
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