Abstract
Massive multi-omics data are being used to research cancer pathogenesis at the molecular level as high-throughput sequencing technology advances. Many present approaches frequently fail to detect strongly coupled modules that are intimately associated with cancer. By combining two forms of omics data, a technique to active bio-module identification known as IdeMod is proposed, which employs gene expression and protein-protein interaction networks. IdeMod is a p-step random walk kernel regression model-based gene activity score algorithm that uses the Pareto optimum consensus (POC) method's dominance connections to generate a prioritised list of genes. IdeMod uses the SA GPROX simulated annealing technique to identify the PPI network's most linked and high-priority bio-modules. The techniques RegMod, LEAN, SigMod, ModFinder, and IdeMod were experimentally tested on real-world cervical and BRCA datasets. These findings show that the IdeMod algorithm may identify a densely linked module containing multiple genes that either promote or hinder tumour growth. The BRCA1 gene increases the likelihood of developing hereditary breast cancer associated with BRCA mutations. As a result, the IdeMod technique can be used in conjunction with other tools to detect bio-modules.
Original language | English |
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Article number | e02466 |
Journal | Scientific African |
Volume | 26 |
DOIs | |
Publication status | Published - Dec 2024 |
Keywords
- Active bio-module identification
- Bioinformatics
- Gene prioritization
- Omics data
- POC
- PPI