Skip to Main content Skip to Navigation
Journal articles

Integration of quantitative proteomics data and interaction networks: Identification of dysregulated cellular functions during cancer progression

Abstract : Quantitative proteomics allows the characterization of molecular changes between healthy and disease states. To interpret such datasets, their integration to the protein-protein interaction network provides a more comprehensive understanding of cellular function dysregulation in diseases than just considering lists of dysregulated proteins. Here, we propose a novel computational method, which combines protein interaction network and statistical analyses to establish expression profiles at the network module level rather than at the individual protein level, and to detect and characterize dysregulated network modules through different stages of cancer progression. We applied our approach to two publicly available datasets as case studies.
Document type :
Journal articles
Complete list of metadatas

https://hal-amu.archives-ouvertes.fr/hal-01406344
Contributor : Andreas Zanzoni <>
Submitted on : Thursday, December 1, 2016 - 9:55:13 AM
Last modification on : Monday, April 8, 2019 - 1:32:04 PM

Identifiers

Collections

Citation

Andreas Zanzoni, Christine Brun. Integration of quantitative proteomics data and interaction networks: Identification of dysregulated cellular functions during cancer progression. Methods, Elsevier, 2016, 93, pp.103 - 109. ⟨10.1016/j.ymeth.2015.09.014⟩. ⟨hal-01406344⟩

Share

Metrics

Record views

130