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Updated: Nov 6, 2025
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Effective utilisation of influence maximization technique for the identification of significant nodes in breast
Hrishikesh Bharadwaj Chakrapani1, Smruti Chourasia1, Sibasish Gupta1
1School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India.
Background:
Identifying the most important genes in a cancer gene network is a crucial step in understanding the disease's functional characteristics and finding an effective drug.
Method:
In this study, a popular influence maximization technique was applied on a large breast cancer gene network to identify the most influential genes computationally. The novel approach involved incorporating gene expression data and protein to protein interaction network to create a customized pruned and weighted gene network. This was then readily provided to the influence maximization procedure. The weighted gene network was also processed through a widely accepted framework that identified essential proteins to benchmark the proposed method.
Results:
The proposed method's results had matched with the majority of the output from the benchmarked framework. The key takeaway from the experiment was that the influential genes identified by the proposed method, which did not match favorably with the widely accepted framework, were found to be very important by previous in-vivo studies on breast cancer.
Interpretation & Conclusion:
The new findings generated from the proposed method give us a favorable reason to infer that influence maximization added a more diversified approach to define and identify important genes and could be incorporated with other popular computational techniques for more relevant results.
Insights
This study identifies key breast cancer genes using influence maximization on gene networks. The novel method highlights important genes, offering a diversified approach for cancer research and drug discovery.
Area of Science:
- Bioinformatics
- Computational Biology
- Cancer Genomics
Background:
- Identifying critical genes in cancer networks is vital for understanding disease mechanisms.
- Effective drug development relies on pinpointing key genes within complex biological networks.
Purpose of the Study:
- To computationally identify the most influential genes in a breast cancer gene network.
- To apply a novel approach integrating gene expression and protein-protein interaction data.
Main Methods:
- Utilized influence maximization on a custom-built, pruned, and weighted breast cancer gene network.
- Incorporated gene expression data and protein-protein interaction networks.
- Benchmarked the proposed method against a widely accepted essential protein identification framework.
Main Results:
- The proposed method's results largely aligned with the benchmark framework.
- Identified influential genes not highlighted by the benchmark but validated by previous in-vivo breast cancer studies.
Conclusions:
- Influence maximization offers a diversified computational approach for identifying critical genes.
- This method can be integrated with other computational techniques for enhanced cancer gene identification and drug discovery.
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