Cluster analysis for the impact of sickle cell disease on the human erythrocyte protein interactome

Larry P. Ammann, Steven Goodman

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Protein interaction networks provide useful information to assess impacts of disease on cell functions. Statistical clustering methods applied to these networks can reveal impacts on particular functional groups of proteins. In addition, clustering methods can identify subsets of proteins that require additional study to provide updated information regarding their position within an interaction network, and hence increase our understanding of their relationships with other proteins in the network. These ideas are illustrated here by considering the impacts of sickle cell disease on the human erythrocyte interaction network. Statistical cluster analyses are performed based on a measure of similarity for nodes within a network called the Generalized Topological Overlap Measure. These analyses identify clusters of proteins that are similar in terms of shared interaction partners. Identification of clusters that contain proteins whose relative abundances have been significantly altered in sickle cell patients provides specific information about the impact of these proteins on erythrocyte functions.

Original languageEnglish (US)
Pages (from-to)703-711
Number of pages9
JournalExperimental Biology and Medicine
Volume234
Issue number6
DOIs
StatePublished - Jun 1 2009

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Cluster analysis
Sickle Cell Anemia
Cluster Analysis
Erythrocytes
Proteins
Protein Interaction Maps
Functional groups

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)

Cite this

Cluster analysis for the impact of sickle cell disease on the human erythrocyte protein interactome. / Ammann, Larry P.; Goodman, Steven.

In: Experimental Biology and Medicine, Vol. 234, No. 6, 01.06.2009, p. 703-711.

Research output: Contribution to journalArticle

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