Skip to Main content Skip to Navigation
Conference papers

Random Walking on Functional Interaction Networks to Rank Genes Involved in Cancer

Abstract : A large scale analysis of gene expression data, performed by Segal and colleagues, identified sets of genes named Cancer Modules (CMs), involved in the onset and progression of cancer. By using functional interaction network data derived from different sources of biomolecular information, we show that random walks and label propagation algorithms are able to correctly rank genes with respect to CMs. In particular, the random walk with restart algorithm (RWR), by exploiting both the global topology of the functional interaction network, and local functional connections between genes relatively close to CM genes, achieves significantly better results than the other compared methods, suggesting that RWR could be applied to discover novel genes involved in the biological processes underlying tumoral diseases.
Document type :
Conference papers
Complete list of metadatas

Cited literature [24 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01523045
Contributor : Hal Ifip <>
Submitted on : Tuesday, May 16, 2017 - 9:16:44 AM
Last modification on : Thursday, March 5, 2020 - 5:41:42 PM
Long-term archiving on: : Friday, August 18, 2017 - 12:51:28 AM

File

978-3-642-33412-2_7_Chapter.pd...
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Matteo Re, Giorgio Valentini. Random Walking on Functional Interaction Networks to Rank Genes Involved in Cancer. 8th International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2012, Halkidiki, Greece. pp.66-75, ⟨10.1007/978-3-642-33412-2_7⟩. ⟨hal-01523045⟩

Share

Metrics

Record views

117

Files downloads

297