Detection of Driver Modules with Rarely Mutated Genes in Cancers

Li, Feng; Gao, Lin; Wang, Bingbo

doi:10.1109/tcbb.2018.2846262

Cited by 13 publications

(10 citation statements)

References 65 publications

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“…There are no new data associated with this article. This data is from the TCGA website (https://portal.gdc.cancer.gov/) and the Catalogue of Somatic Mutations in Cancer (COSMIC) (https://portal.gdc.cancer.gov/) [23] and The pan-cancer is from reference [50]. .In addition , the code of our are available at https://github.com/FengLi12/Our-code…”

Section: Ethics Approval and Consent To Participatementioning

confidence: 99%

Network Embedding Framework for Driver Gene Discovery by Combining Functional and Structural Information

Chu

Shang

et al. 2022

Preprint

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Comprehensive analysis of multiple data sets can identify potential driver genes for various cancers. In recent years, driver gene discovery based on massive mutation data and gene interaction networks has attracted increasing attention, but there is still a need to explore combining functional and structural information of genes in protein interaction networks to identify driver genes. There, we propose a network embedding framework combining functional and structural information to identify driver genes. Firstly, we combine the mutation data and gene interaction networks to construct mutation integration network using network propagation algorithm. Secondly, the struc2vec model is used for extracting gene features from the mutation integration network, which contains both genes functional and structural information. Finally, machine learning algorithms are utilized to identify the driver genes. At the same time, we also conduct comparative analysis of three gene interaction networks, three gene standard sets, and five machine learning algorithms. Compared with the previous four excellent methods, our method can find gene pairs that are distant from each other through structural similarities and has better performance in identifying driver genes for 12 cancers in the cancer genome atlas. Our framework provides a new perspective for feature selection to identify novel driver genes.

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Section: Ethics Approval and Consent To Participatementioning

confidence: 99%

Network Embedding Framework for Driver Gene Discovery by Combining Functional and Structural Information

Chu

Shang

et al. 2022

Preprint

Self Cite

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“…To evaluate the accuracy of the driver modules detected by ECSWalk, this study uses the Accuracy and F-measure evaluation indices to measure the accuracy of the driver modules based on the known pathways [24]. The higher the Accuracy value, the better the classification effect.…”

Section: Comparison Of Module Accuracymentioning

confidence: 99%

Integrating Protein–Protein Interaction Networks and Somatic Mutation Data to Detect Driver Modules in Pan-Cancer

Chen

et al. 2021

Interdiscip Sci Comput Life Sci

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Background: With the constant update of large-scale sequencing data and the continuous improvement of cancer genomics data such as the cancer genome atlas ICGC and TCGA, it gains increasing importance how to detect the functional high-frequency mutation gene set in cells that causes cancer within the field of medicine.Methods: In this study, to solve the issue of mutated gene heterogeneity and improve the accuracy of driver modules, we propose a new recognition method of driver modules, named ECSWalk, based on the human protein interaction networks and pan-cancer somatic mutation data. This study firstly utilizes high mutual exclusivity and high coverage between mutation genes and topological structure similarity of the nodes in complex networks to calculate interaction weights between genes. Secondly, the method of random walk with restart is utilized to construct a weighted directed network, and the strong connectivity principle of the directed graph is utilized to create the initial candidate modules with a certain number of genes. Finally, the large modules in the candidate modules are reasonably split using the way of the induced subgraph, and the small modules are expanded using a greedy strategy to obtain the optimal driver modules.Results: This method is applied to the analysis of TCGA pan-cancer data, and the experimental results show that ECSWalk can detect driver modules more effectively and accurately, and can identify new candidate gene sets with higher biological relevance and statistical significance than MEXCOWalk and HotNet2.Conclusions: ECSWalk is of theoretical guidance and practical value for cancer diagnosis, treatment and drug targets.

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“…Network. Gene interaction network contains modules, defined as groups of genes as nodes, and the gene interactions are relatively denser within the same module in comparison to the interactions between different modules (the connections of nodes between modules are sparser relatively) [24,25]. Considering the independence between the membership of genes to network modules and mutation frequencies or direct connections of genes, we utilize the network modules as features for discriminating cancer driver genes in our study.…”

Section: Modularity Of Interactionmentioning

confidence: 99%

Driver Attribute Filling for Genes in Interaction Network via Modularity Subspace-Based Concept Learning from Small Samples

Xie

Duan

2020

Complexity

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The aberrations of a gene can influence it and the functions of its neighbour genes in gene interaction network, leading to the development of carcinogenesis of normal cells. In consideration of gene interaction network as a complex network, previous studies have made efforts on the driver attribute filling of genes via network properties of nodes and network propagation of mutations. However, there are still obstacles from problems of small size of cancer samples and the existence of drivers without property of network neighbours, limiting the discovery of cancer driver genes. To address these obstacles, we propose an efficient modularity subspace based concept learning model. Our model can overcome the curse of dimensionality due to small samples via dimension reduction in the task of attribute concept learning and explore the features of genes through modularity subspace beyond the network neighbours. The evaluation analysis also demonstrates the superiority of our model in the task of driver attribute filling on two gene interaction networks. Generally, our model shows a promising prospect in the application of interaction network analysis of tumorigenesis.

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Detection of Driver Modules with Rarely Mutated Genes in Cancers

Cited by 13 publications

References 65 publications

Network Embedding Framework for Driver Gene Discovery by Combining Functional and Structural Information

Network Embedding Framework for Driver Gene Discovery by Combining Functional and Structural Information

Integrating Protein–Protein Interaction Networks and Somatic Mutation Data to Detect Driver Modules in Pan-Cancer

Driver Attribute Filling for Genes in Interaction Network via Modularity Subspace-Based Concept Learning from Small Samples

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