Discovering mutated driver genes through a robust and sparse co-regularized matrix factorization framework with prior information from mRNA expression patterns and interaction network

Xi, Jianing; Wang, Minghui; Li, Ao

doi:10.1186/s12859-018-2218-y

Cited by 27 publications

(12 citation statements)

References 84 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, two significant GO biological processes, antigen processing and presentation and interferon-gamma-mediated signaling pathway, are both essential for immune response, which is often observed to be inhibited in the tumor microenvironment [ 7 , 32 ]. In addition, we test the relationship between the functional modules and cancer driver genes [ 33 , 34 ]. By following a previous work [ 35 ], we utilized 2,372 genes from the Network of Cancer Genes (NCG) [ 36 ] as benchmarking cancer genes, including 711 known cancer genes from the Cancer Gene Census (CGC) [ 37 ].…”

Section: Resultsmentioning

confidence: 99%

Feature related multi-view nonnegative matrix factorization for identifying conserved functional modules in multiple biological networks

Wang

Gao

et al. 2018

BMC Bioinformatics

View full text Add to dashboard Cite

BackgroundComprehensive analyzing multi-omics biological data in different conditions is important for understanding biological mechanism in system level. Multiple or multi-layer network model gives us a new insight into simultaneously analyzing these data, for instance, to identify conserved functional modules in multiple biological networks. However, because of the larger scale and more complicated structure of multiple networks than single network, how to accurate and efficient detect conserved functional biological modules remains a significant challenge.ResultsHere, we propose an efficient method, named ConMod, to discover conserved functional modules in multiple biological networks. We introduce two features to characterize multiple networks, thus all networks are compressed into two feature matrices. The module detection is only performed in the feature matrices by using multi-view non-negative matrix factorization (NMF), which is independent of the number of input networks. Experimental results on both synthetic and real biological networks demonstrate that our method is promising in identifying conserved modules in multiple networks since it improves the accuracy and efficiency comparing with state-of-the-art methods. Furthermore, applying ConMod to co-expression networks of different cancers, we find cancer shared gene modules, the majority of which have significantly functional implications, such as ribosome biogenesis and immune response. In addition, analyzing on brain tissue-specific protein interaction networks, we detect conserved modules related to nervous system development, mRNA processing, etc.ConclusionsConMod facilitates finding conserved modules in any number of networks with a low time and space complexity, thereby serve as a valuable tool for inference shared traits and biological functions of multiple biological system.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2434-5) contains supplementary material, which is available to authorized users.

show abstract

Section: Resultsmentioning

confidence: 99%

Feature related multi-view nonnegative matrix factorization for identifying conserved functional modules in multiple biological networks

Wang

Gao

et al. 2018

BMC Bioinformatics

View full text Add to dashboard Cite

show abstract

“…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.

View full text Add to dashboard Cite

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.

show abstract

“…To solve the problem of inaccessibility of information, the margin writing of the subgroups, as Driversub method was proposed. Correspondingly, in this method, an unsupervised learning method was used, which needs no information about subgroup [13]. One of the challenges in analyzing the results of this method is that the available data has noise and there is still discarded data, which consequently affects the accuracy of the results.…”

Section: Introductionmentioning

confidence: 99%

Providing An Optimized Model to Detect Driver Genes From Heterogeneous Cancer Samples, Using Restriction in Subspace Learning

Ebadi

Soleimani

Ghaderzadeh

2020

Preprint

View full text Add to dashboard Cite

Extracting the drivers from genes with mutation, and segregation of driver and passenger genes are known as the most controversial issues in cancer studies. According to the heterogeneity of cancer, it is not possible to identify indicators under a group of associated drivers, in order to identify a group of patients with diseases related to these subgroups. Therefore, the precise identification of the related driver genes using artificial intelligence techniques is still considered as a challenge for researchers. In this research, a new method has been developed using the subspace learning method, unsupervised learning, and with more constraints. Accordingly, it has been attempted to extract the driver genes with more precision and accurate results. The obtained results show that the proposed method is more efficient for more genes compared to other methods. The p-value of the proposed method was 9.21e-7 better than previous methods for 200 genes. The results show that the p. value of the proposed method is about 2.7 times less than the driver sub method, indicating that the proposed method can identify driver genes in cancerous tumors with greater accuracy and reliability.

show abstract

Discovering mutated driver genes through a robust and sparse co-regularized matrix factorization framework with prior information from mRNA expression patterns and interaction network

Cited by 27 publications

References 84 publications

Feature related multi-view nonnegative matrix factorization for identifying conserved functional modules in multiple biological networks

Feature related multi-view nonnegative matrix factorization for identifying conserved functional modules in multiple biological networks

An Effective Graph Clustering Method to Identify Cancer Driver Modules

Providing An Optimized Model to Detect Driver Genes From Heterogeneous Cancer Samples, Using Restriction in Subspace Learning

Contact Info

Product

Resources

About