4.6 Article Proceedings Paper

Predicting stage-specific cancer related genes and their dynamic modules by integrating multiple datasets

Journal

BMC BIOINFORMATICS
Volume 20, Issue -, Pages -

Publisher

BMC
DOI: 10.1186/s12859-019-2740-6

Keywords

Disease genes; Clinical stages; Dynamic modules; Pathway networks; Disease evolution

Funding

  1. National Natural Science Foundation of China [61602386, 61772426, 61332014]
  2. Natural Science Foundation of Shaanxi Province [2017JQ6008]
  3. Top International University Visiting Program for Outstanding Young scholars of Northwestern Polytechnical University

Ask authors/readers for more resources

BackgroundThe mechanism of many complex diseases has not been detected accurately in terms of their stage evolution. Previous studies mainly focus on the identification of associations between genes and individual diseases, but less is known about their associations with specific disease stages. Exploring biological modules through different disease stages could provide valuable knowledge to genomic and clinical research.ResultsIn this study, we proposed a powerful and versatile framework to identify stage-specific cancer related genes and their dynamic modules by integrating multiple datasets. The discovered modules and their specific-signature genes were significantly enriched in many relevant known pathways. To further illustrate the dynamic evolution of these clinical-stages, a pathway network was built by taking individual pathways as vertices and the overlapping relationship between their annotated genes as edges.ConclusionsThe identified pathway network not only help us to understand the functional evolution of complex diseases, but also useful for clinical management to select the optimum treatment regimens and the appropriate drugs for patients.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available