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• W1970783519 abstract "Functional characterization of proteins belonging to the MHC I superfamily involves knowing their cognate ligands, which can be peptides, lipids or none. However, the experimental identification of these ligands is not an easy task and generally requires some a priori knowledge of their chemical nature (ligand-type specificity). Here, we trained k-nearest neighbor and support vector machine classifiers that predict the ligand-type specificity MHC I proteins with great accuracy. Moreover, we applied these classifiers to human and mouse MHC I proteins of uncharacterized ligands, obtaining some results that can be instrumental to unravel the function of these proteins." @default.
• W1970783519 created "2016-06-24" @default.
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• W1970783519 creator A5051034667 @default.
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• W1970783519 date "2011-10-10" @default.
• W1970783519 modified "2023-10-05" @default.
• W1970783519 title "Recognition of the ligand-type specificity of classical and non-classical MHC I proteins" @default.
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• W1970783519 doi "https://doi.org/10.1016/j.febslet.2011.10.007" @default.
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