Pathway Enrichment Analysis with Networks

dc.contributor.authorLiu, Lu
dc.contributor.authorWei, Jinmao
dc.contributor.authorRuan, Jianhua
dc.date.accessioned2021-04-19T15:04:09Z
dc.date.available2021-04-19T15:04:09Z
dc.date.issued2017-09-28
dc.date.updated2021-04-19T15:04:10Z
dc.description.abstractDetecting associations between an input gene set and annotated gene sets (e.g., pathways) is an important problem in modern molecular biology. In this paper, we propose two algorithms, termed NetPEA and NetPEA’, for conducting network-based pathway enrichment analysis. Our algorithms consider not only shared genes but also gene–gene interactions. Both algorithms utilize a protein–protein interaction network and a random walk with a restart procedure to identify hidden relationships between an input gene set and pathways, but both use different randomization strategies to evaluate statistical significance and as a result emphasize different pathway properties. Compared to an over representation-based method, our algorithms can identify more statistically significant pathways. Compared to an existing network-based algorithm, EnrichNet, our algorithms have a higher sensitivity in revealing the true causal pathways while at the same time achieving a higher specificity. A literature review of selected results indicates that some of the novel pathways reported by our algorithms are biologically relevant and important. While the evaluations are performed only with KEGG pathways, we believe the algorithms can be valuable for general functional discovery from high-throughput experiments.
dc.description.departmentComputer Science
dc.identifierdoi: 10.3390/genes8100246
dc.identifier.citationGenes 8 (10): 246 (2017)
dc.identifier.urihttps://hdl.handle.net/20.500.12588/392
dc.rightsAttribution 4.0 United States
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectpathway
dc.subjectprotein–protein interaction network
dc.subjectenrichment analysis
dc.subjectgene sets
dc.subjectrandom walk with restart
dc.titlePathway Enrichment Analysis with Networks
dc.typeArticle

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
genes-08-00246.pdf
Size:
1013.21 KB
Format:
Adobe Portable Document Format

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
0 B
Format:
Item-specific license agreed upon to submission
Description: