IDM-SPS: Identifying driver module with somatic mutation, PPI network and subcellular localization

Wu, Jingli; Yang, Jifan; Li, Gaoshi; Wang, Jinyan

doi:10.1016/j.engappai.2021.104482

Cited by 6 publications

(2 citation statements)

References 49 publications

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“…Hence, some pathways containing rare mutations may be ignored [ 16 ]. Prior knowledge-based methods regard genes with high mutation rates and their less-frequently mutated neighbors as drivers, and attempt to detect them from known gene-level or protein-level pathways or networks [ 17 ], such as MEXCOwalk [ 16 ], HotNet [ 18 ], IDM-SPS [ 19 ] and HotNet2 [ 20 ]. However, biological networks are still associated with noise and incomplete.…”

Section: Introductionmentioning

confidence: 99%

Identifying driver pathways based on a parameter-free model and a partheno-genetic algorithm

Nie

et al. 2023

BMC Bioinformatics

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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.

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Section: Introductionmentioning

confidence: 99%

Identifying driver pathways based on a parameter-free model and a partheno-genetic algorithm

Nie

et al. 2023

BMC Bioinformatics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Hence, some pathways containing rare mutations may be ignored [12]. Prior knowledge-based methods regard genes with high mutation rates and their less-frequently mutated neighbors as drivers, and attempt to detect them from known gene or protein-level pathways or networks [13], such as MEXCOwalk [12], HotNet [14], IDM-SPS [15] and HotNet2 [16]. However, biological networks are still associated with noise and incomplete.…”

Section: Introductionmentioning

confidence: 99%

Identifying driver pathways based on a parameter-free model and a partheno-genetic algorithm

Nie

et al. 2022

Preprint

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Background: Tremendous amounts of omics data accumulated has 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. The integration of multiple omics data to identify cancer driving pathways is a challenging problem. Results: In this paper, 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 set recognized by the proposed CPGA-SMCMN method possesses 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 method CPGA-SMCMN and six state-of-the-art ones.

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ICDM-GEHC: identifying cancer driver module based on graph embedding and hierarchical clustering

Deng,

Wu,

et al. 2024

Complex Intell. Syst.

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Due to the high heterogeneity of cancers, it is rather essential to explore driver modules with the help of gene mutation data as well as known interactions between genes/proteins. Unfortunately, latent false positive interactions are inevitable in the Protein-Protein Interaction (PPI) network. Hence in the presented method, a new weight evaluation index, based on the gene-microRNA network as well as somatic mutation profile, is introduced for weighting the PPI network first. Subsequently, the vertices in the weighted PPI network are hierarchically clustered by measuring the Mahalanobis distance of their feature vectors, extracted with the graph embedding method Node2vec. Finally, a heuristic process with dropping and extracting is conducted on the gene clusters to produce a group of gene modules. Numerous experiment results demonstrate that the proposed method exhibits superior performance to four cutting-edge identification methods in most cases regarding the capability of recognizing the acknowledged cancer-related genes, generating modules having relatively high coverage and mutual exclusivity, and are significantly enriched for specific types of cancers. The majority of the genes in the identified modules are involved in cancer-related signaling pathways, or have been reported to be carcinogenic in the literature. Furthermore, many cancer related genes detected by the proposed method are actually omitted by the four comparison methods, which has been verified in the experiments.

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IDM-SPS: Identifying driver module with somatic mutation, PPI network and subcellular localization

Cited by 6 publications

References 49 publications

Identifying driver pathways based on a parameter-free model and a partheno-genetic algorithm

Identifying driver pathways based on a parameter-free model and a partheno-genetic algorithm

Identifying driver pathways based on a parameter-free model and a partheno-genetic algorithm

ICDM-GEHC: identifying cancer driver module based on graph embedding and hierarchical clustering

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