Identifying driver genes in cancer based on Pareto optimality consensus
- Resource Type
- Conference
- Authors
- Deng, Zheng; Wu, Jingli; Chen, Xiaorong; Li, Gaoshi
- Source
- 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Bioinformatics and Biomedicine (BIBM), 2022 IEEE International Conference on. :740-743 Dec, 2022
- Subject
- Bioengineering
Computing and Processing
Signal Processing and Analysis
Weight measurement
Databases
Medical treatment
Genomics
Pareto optimization
Biomarkers
Nonhomogeneous media
driver gene prioritization
random walk
gene expression
multi-omics data
- Language
An important issue in cancer genomics is the identification of driver genes. It is significant for the discovery of key biomarkers and the development of effective personalized therapies. In this paper, a computated method PGScore is proposed. It scores genes at multilayer and integrates the scores to identify cancer driver genes based on Pareto Optimality Consensus(POC) strategy. PGScore uses random walks to reevaluate gene mutations, and integrates differential expression of mRNA and miRNA in normal and cancer samples. It measures the centrality of the gene in the network according to the weight of its direct and indirect neighbors, and finally integrates the above layers to get the final priority of the genes. We compare PGScore with state-of-the-art cancer driver genes prioritization methods on two real cancer datasets. The results show that PGScore can obtain better performance in identification accuracy and the partial area under the ROC(pAUC) curve on multiple reference databases.