Assessment of Hierarchical Clustering Methodologies for Proteomic Data Mining-Reference-Cited by-同舟云学术

Assessment of Hierarchical Clustering Methodologies for Proteomic Data Mining

Published:2006-12-14 Issue:1 Volume:6 Page:358-366
ISSN:1535-3893
Container-title:Journal of Proteome Research
language:en
Short-container-title:J. Proteome Res.

Author:

Meunier Bruno¹,Dumas Emilie¹,Piec Isabelle¹,Béchet Daniel¹,Hébraud Michel¹,Hocquette Jean-François¹

Affiliation:

1. UR 1213, Unité de Recherches sur les Herbivores, Equipe Croissance et Métabolisme du Muscle, INRA de Clermont-Ferrand/Theix, F-63122 Saint-Genès Champanelle, France, UR454 Microbiologie, Equipe Qualité et Sécurité des Aliments (QuaSA), INRA de Clermont-Ferrand/Theix, F-63122 Saint-Genès Champanelle, France, Laboratoire d'Immunologie EMI 0351, INSERM, 3 rue des Louvels, F-80036 Amiens, France, UMR 1019, Unité de Nutrition Humaine, INRA de Clermont-Ferrand/Theix, F-63122 Saint-Genès Champanelle, France,...

Publisher

American Chemical Society (ACS)

Subject

General Chemistry,Biochemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/pr060343h

Reference29 articles.

1. Processing of Data Generated by 2-Dimensional Gel Electrophoresis for Statistical Analysis: Missing Data, Normalization, and Statistics

2. Data analysis methods for detection of differential protein expression in two-dimensional gel electrophoresis

3. Molecular Classification and Survival Prediction in Human Gliomas Based on Proteome Analysis

4. Cluster analysis of an extensive human breast cancer cell line protein expression map database

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