Identifying driver pathways based on a parameter-free model and a partheno-genetic algorithm
Abstract Background Tremendous amounts of omics data accumulated have made it possible to identify cancer driver pathways through computational methods, which is believed to be able to offer critical information in such downstream research as ascertaining cancer pathogenesis, developing anti-cancer...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-05-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-023-05319-8 |
| _version_ | 1797817745353474048 |
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| author | Jingli Wu Qinghua Nie Gaoshi Li Kai Zhu |
| author_facet | Jingli Wu Qinghua Nie Gaoshi Li Kai Zhu |
| author_sort | Jingli Wu |
| collection | DOAJ |
| description | Abstract Background Tremendous amounts of omics data accumulated have made it possible to identify cancer driver pathways through computational methods, which is believed to be able to offer critical information in such downstream research as ascertaining cancer pathogenesis, developing anti-cancer drugs, and so on. It is a challenging problem to identify cancer driver pathways by integrating multiple omics data. Results In this study, a parameter-free identification model SMCMN, incorporating both pathway features and gene associations in Protein–Protein Interaction (PPI) network, is proposed. A novel measurement of mutual exclusivity is devised to exclude some gene sets with “inclusion” relationship. By introducing gene clustering based operators, a partheno-genetic algorithm CPGA is put forward for solving the SMCMN model. Experiments were implemented on three real cancer datasets to compare the identification performance of models and methods. The comparisons of models demonstrate that the SMCMN model does eliminate the “inclusion” relationship, and produces gene sets with better enrichment performance compared with the classical model MWSM in most cases. Conclusions The gene sets recognized by the proposed CPGA-SMCMN method possess more genes engaging in known cancer related pathways, as well as stronger connectivity in PPI network. All of which have been demonstrated through extensive contrast experiments among the CPGA-SMCMN method and six state-of-the-art ones. |
| first_indexed | 2024-03-13T08:58:00Z |
| format | Article |
| id | doaj.art-05549e7df74f45fea6ae4a7a79f240ec |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-03-13T08:58:00Z |
| publishDate | 2023-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-05549e7df74f45fea6ae4a7a79f240ec2023-05-28T11:29:06ZengBMCBMC Bioinformatics1471-21052023-05-0124112510.1186/s12859-023-05319-8Identifying driver pathways based on a parameter-free model and a partheno-genetic algorithmJingli Wu0Qinghua Nie1Gaoshi Li2Kai Zhu3Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal UniversityKey Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal UniversityKey Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal UniversityGuangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal UniversityAbstract Background Tremendous amounts of omics data accumulated have made it possible to identify cancer driver pathways through computational methods, which is believed to be able to offer critical information in such downstream research as ascertaining cancer pathogenesis, developing anti-cancer drugs, and so on. It is a challenging problem to identify cancer driver pathways by integrating multiple omics data. Results In this study, a parameter-free identification model SMCMN, incorporating both pathway features and gene associations in Protein–Protein Interaction (PPI) network, is proposed. A novel measurement of mutual exclusivity is devised to exclude some gene sets with “inclusion” relationship. By introducing gene clustering based operators, a partheno-genetic algorithm CPGA is put forward for solving the SMCMN model. Experiments were implemented on three real cancer datasets to compare the identification performance of models and methods. The comparisons of models demonstrate that the SMCMN model does eliminate the “inclusion” relationship, and produces gene sets with better enrichment performance compared with the classical model MWSM in most cases. Conclusions The gene sets recognized by the proposed CPGA-SMCMN method possess more genes engaging in known cancer related pathways, as well as stronger connectivity in PPI network. All of which have been demonstrated through extensive contrast experiments among the CPGA-SMCMN method and six state-of-the-art ones.https://doi.org/10.1186/s12859-023-05319-8CancerDriver pathwayProtein–Protein interactionPartheno-genetic algorithm |
| spellingShingle | Jingli Wu Qinghua Nie Gaoshi Li Kai Zhu Identifying driver pathways based on a parameter-free model and a partheno-genetic algorithm BMC Bioinformatics Cancer Driver pathway Protein–Protein interaction Partheno-genetic algorithm |
| title | Identifying driver pathways based on a parameter-free model and a partheno-genetic algorithm |
| title_full | Identifying driver pathways based on a parameter-free model and a partheno-genetic algorithm |
| title_fullStr | Identifying driver pathways based on a parameter-free model and a partheno-genetic algorithm |
| title_full_unstemmed | Identifying driver pathways based on a parameter-free model and a partheno-genetic algorithm |
| title_short | Identifying driver pathways based on a parameter-free model and a partheno-genetic algorithm |
| title_sort | identifying driver pathways based on a parameter free model and a partheno genetic algorithm |
| topic | Cancer Driver pathway Protein–Protein interaction Partheno-genetic algorithm |
| url | https://doi.org/10.1186/s12859-023-05319-8 |
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