Identification of mutated driver pathways in cancer using a multi-objective optimization model

Zheng, Chun-Hou; Wu, Yang; Chong, Yanwen; Xia, Junfeng

doi:10.1016/j.compbiomed.2016.03.002

Cited by 22 publications

(27 citation statements)

References 36 publications

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“…In the experiments, real biological datasets as well as simulated ones were leveraged to compare the identification performance of the Dendrix [12], the GA [14], the iMCMC [15], the MOGA [16], the PGA-MWS [17]…”

Section: Resultsmentioning

confidence: 99%

“…Zhang et al [15] presented a network-based approach iMCMC by integrating copy number variations (CNVs), somatic mutations, and gene expressions. Zheng et al [16] devised a more reliable algorithm MOGA by coordinating high coverage and high mutual exclusivity. Recently, Wu et al [17] redefined the model of the maximum weight submatrix problem, modulating coverage and mutual exclusivity by using the average weight of genes in one pathway.…”

Section: Introductionmentioning

confidence: 99%

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A model and algorithm for identifying driver pathways based on weighted non-binary mutation matrix

Zhu

et al. 2021

Appl Intell

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It is generally acknowledged that driver pathway plays a decisive role in the occurrence and progress of tumors, and the identification of driver pathways has become imperative for precision medicine or personalized medicine. Due to the inevitable sequencing error, the noise contained in single omics cancer data usually plays a negative effect on identification. It is a feasible approach to take advantage of multi-omics cancer data rather than a single one now that large amounts of multi-omics cancer data have become available. The identification of driver pathways by integrating multi-omics cancer data has attracted attention of researchers in bioinformatics recently. In this paper, a weighted non-binary mutation matrix is constructed by integrating copy number variations, somatic mutations and gene expressions. Based on the weighted non-binary mutation matrix, a new identification model is proposed through defining new measurements of coverage and exclusivity. Then, a cooperative coevolutionary algorithm CGA-MWS is put forward for solving the presented model. Both real cancer data and simulated one were used to conduct comparisons among methods Dendrix, GA, iMCMC, MOGA, PGA-MWS and CGA-MWS. Compared with the pathways identified by the other five methods, more genes, belonging to the pathway identified by the CGA-MWS method, are enriched in a known signaling pathway in most cases. Simultaneously, the high efficiency of method CGA-MWS makes it practical in realistic applications. All of which have been verified through a number of experiments.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A model and algorithm for identifying driver pathways based on weighted non-binary mutation matrix

Zhu

et al. 2021

Appl Intell

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“…One of the challenges of cancer treatment is how to identify tumor subtypes, which can help to provide patients with specific treatment. Meanwhile, with the continuous development of all kinds of sequencing technologies, a lot of high flux data have been produced (Zheng et al 2016). For cancer subtypes identification, integration of different types of omics data to unravel the molecular mechanism of complex diseases becomes more and more important (Zhang et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Clustering of Cancer Data Based on Stiefel Manifold for Multiple Views

Tian

Zhao

Zheng

2021

Preprint

Self Cite

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Background: In recent years, various sequencing techniques have been used to collect biomedical omics datasets. It is usually possible to obtain multiple types of omics data from a single patient sample. Clustering of these datasets has proved to be valuable for biological and medical research and helpful to reveal data structures from multiple collections. However, such data often have small sample size and high dimension. It is difficult to find a suitable integration method for structural analysis of multiple datasets. Results: In this paper, a multi-view clustering based on Stiefel manifold method (MCSM) is proposed. Firstly, we established a binary optimization model for the simultaneous clustering problem. Secondly, the optimization problem solved by linear search algorithm based on Stiefel manifold. Finally, we integrated the clustering results obtained from three omics by using k-nearest neighbor method. We applied this approach to four cancer datasets on TCGA. The result shows that our method is superior to several state-of-art methods, which depends on the hypothesis that the underlying omics cluster class is the same.Conclusion: Particularly, our approach has better performs when the underlying clusters are inconsistent. For patients with different subtypes, both consistent and differential clusters can be identified at the same time.

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“…Functionally related driver mutations in the genome, also known as driver modules or pathways, activate the mechanisms by which cancer occurs, triggering cancer, driving cancer progression and giving cancer cells a selective advantage. Some computational methods and mathematical models have been developed to detect driver gene sets, pathways and modules by using large-scale sequencing data (Hou et al, 2016;Zheng et al, 2016;Yang et al, 2017;Xi et al, 2018;Ahmed et al, 2019;Deng et al, 2019;Zhang and Wang, 2019a;Pelegrina et al, 2020). Existing research show that the members of cancer driver modules often exhibit specific mutation patterns in cancer samples, the most significant characteristic is mutual exclusivity (mutex) which means once one member mutates, the tumor will gain a significant selection advantage, while later mutations in other members will not give the tumor a selection advantage.…”

Section: Introductionmentioning

confidence: 99%

An Effective Graph Clustering Method to Identify Cancer Driver Modules

Zhang

Zeng

Wang

et al. 2020

Front. Bioeng. Biotechnol.

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Identifying the molecular modules that drive cancer progression can greatly deepen the understanding of cancer mechanisms and provide useful information for targeted therapies. Most methods currently addressing this issue primarily use mutual exclusivity without making full use of the extra layer of module property. In this paper, we propose MCLCluster to identity cancer driver modules, which use somatic mutation data, Cancer Cell Fraction (CCF) data, gene functional interaction network and protein-protein interaction (PPI) network to derive the module property on mutual exclusivity, connectivity in PPI network and functionally similarity of genes. We have taken three effective measures to ensure the effectiveness of our algorithm. First, we use CCF data to choose stronger signals and more confident mutations. Second, the weighted gene functional interaction network is used to quantify the gene functional similarity in PPI. The third, graph clustering method based on Markov is exploited to extract the candidate module. MCLCluster is tested in the two TCGA datasets (GBM and BRCA), and identifies several well-known oncogenes driver modules and some modules with functionally associated driver genes. Besides, we compare it with Multi-Dendrix, FSME Cluster and RME in simulated dataset with background noise and passenger rate, MCLCluster outperforming all of these methods.

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Identification of mutated driver pathways in cancer using a multi-objective optimization model

Cited by 22 publications

References 36 publications

A model and algorithm for identifying driver pathways based on weighted non-binary mutation matrix

A model and algorithm for identifying driver pathways based on weighted non-binary mutation matrix

Clustering of Cancer Data Based on Stiefel Manifold for Multiple Views

An Effective Graph Clustering Method to Identify Cancer Driver Modules

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