ArticleIET systems biology2016
Signalling pathway impact analysis based on the strength of interaction between genes.
Article in IET systems biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review.Computational and structural biotechnology journal · 2024Review
- The GJB3 correlates with the prognosis, immune cell infiltration, and therapeutic responses in lung adenocarcinoma.Open medicine (Warsaw, Poland) · 2024Article
- Analysis of Breast Cancer Based on the Dysregulated Network.Frontiers in genetics · 2022Article
- Identifying disease-associated signaling pathways through a novel effector gene analysis.PeerJ · 2020Article
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Authors and funding
6 authors.
Funding
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Abstract
Signalling pathway analysis is a popular approach that is used to identify significant cancer-related pathways based on differentially expressed genes (DEGs) from biological experiments. The main advantage of signalling pathway analysis lies in the fact that it assesses both the number of DEGs and the propagation of signal perturbation in signalling pathways. However, this method simplifies the interactions between genes by categorising them only as activation (+1) and suppression (-1), which does not encompass the range of interactions in real pathways, where interaction strength between genes may vary. In this study, the authors used newly developed signalling pathway impact analysis (SPIA) methods, SPIA based on Pearson correlation coefficient (PSPIA), and mutual information (MSPIA), to measure the interaction strength between pairs of genes. In analyses of a colorectal cancer dataset, a lung cancer dataset, and a pancreatic cancer dataset, PSPIA and MSPIA identified more candidate cancer-related pathways than were identified by SPIA. Generally, MSPIA performed better than PSPIA.
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