A model and cooperative co-evolution algorithm for identifying driver pathways based on the integrated data and PPI network. (February 2023)

• Record Type: Journal Article
• Authors: Zhu, Kai; Wu, Jingli; Li, Gaoshi; Chen, Xiaorong; Luo, Michael Yourong
• Is Part Of: Expert systems with applications. Volume 212(2023)
• Publication Date: 2023-02
• Subjects: SNVs Single Nucleotide Variations -- CNVs Copy Number Variations -- SVs Structural Variations -- DNA DeoxyriboNucleic Acid -- PPI Protein–Protein Interaction -- GA Genetic Algorithm -- PSO Particle Swarm Optimization -- GGI Gene–Gene Interactions -- CV Coefficient of Variation -- GBM GlioBlastoma -- OV Ovarian Cancer -- THCA Thyroid Carcinoma -- TCGA The Cancer Genome Atlas -- KEGG Kyoto Encyclopedia of Genes and Genomes -- COSMIC Catalogue Of Somatic Mutations In Cancer -- CPU Central Processing Unit -- RAM Random access memory -- GB Gigabyte -- miRNA micro Ribonucleic acid; 00-01 -- 99-00; Driver pathway -- Cancer omics data -- PPI network -- Model -- Genetic algorithm -- Particle swarm optimization; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.118753
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: Driver pathways have been acknowledged to play critical roles in the initiation and progression of cancers►hence it is essential for precision medicine related studies to develop accurate and efficient methods to identify them. Although previous approaches have shown promising results by integrating multi-omics data, their preset artificial parameters may decrease the convenience to use and limit the application scalability. In this paper, a novel integration approach is proposed to incorporate four omics data, i.e., construct a weighted non-binary mutation matrix without presetting artificial parameters. A parameter-free identification model is put forward based on it. It takes advantage of the association between genes in the PPI network as well as balances the contribution of coverage and mutual exclusivity with the harmonic mean. Furthermore, a cooperative co-evolution algorithm is proposed for solving this model. In the algorithm, a particle swarm optimization algorithm suitable for solving combinatorial problems is presented. Three cooperative operators are devised to construct the cooperation among the populations and the swarm to increase the population diversity. Both real biological datasets and simulated ones were exerted to perform experimental comparisons among the proposed method and six other state-of-the-art ones. The gene sets identified by the presented method generally contain more genes involved in known signaling pathways than those obtained by the other methods. Simultaneously, both high accuracy and high efficiency of the proposed method were verified in experiments, making it practical in realistic applications and an effective supplementary tool to identify driver pathways. Highlights: A non-binary mutation matrix is constructed without artificial adjustable parameters. The model incorporates gene association and balances coverage and mutual exclusivity. A PSO algorithm suitable for solving combinatorial problems is presented. A co-operative co-evolution algorithm is put forward for solving the presented model. The performance is compared among the proposed method and seven state of the art ones.