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Jean Garnier

Researcher at Institut national de la recherche agronomique

Publications -  69
Citations -  9481

Jean Garnier is an academic researcher from Institut national de la recherche agronomique. The author has contributed to research in topics: Receptor & Peptide sequence. The author has an hindex of 31, co-authored 69 publications receiving 9329 citations. Previous affiliations of Jean Garnier include University of Paris.

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Journal ArticleDOI

Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins.

TL;DR: The algorithm is shown to be at least as good as, and usually superior to, the reported prediction methods assessed in the same way and the implication in protein folding is discussed.
Book ChapterDOI

GOR method for predicting protein secondary structure from amino acid sequence.

TL;DR: The Garnier-Osguthorpe-Robson method for predicting protein secondary structure from amino acid sequence has the advantage over neural network-based methods or nearest-neighbor methods in that it clearly identifies what is taken into account for the prediction and what is neglected.
Journal ArticleDOI

Cloning and sequencing of porcine LH-hCG receptor cDNA: variants lacking transmembrane domain.

TL;DR: Hydropathy analysis suggests the existence of seven transmembrane domains that show homology with the corresponding regions of other G protein-coupled receptors.
Journal ArticleDOI

Further developments of protein secondary structure prediction using information theory: New parameters and consideration of residue pairs☆

TL;DR: This new version of the GOR method increases the accuracy of prediction by 7%, bringing the amount of residues correctly predicted to 63% for three states and 68 proteins, each protein to be predicted being removed from the database and the parameters derived from the other proteins.
Journal ArticleDOI

An algorithm for secondary structure determination in proteins based on sequence similarity

TL;DR: An empirically determined similarity matrix which assigns a sequence similarity score between any two sequences of 7 residues in length is proposed which had a prediction accuracy of 62.2% over 3 states for 61 proteins and 63.6% for a new set of 7 proteins not in the original data base.