An efficient and effective method to identify significantly perturbed subnetworks in cancer

Yang, Le; Chen, Runpu; Goodison, Steve; Sun, Yijun

doi:10.1038/s43588-020-00009-4

Cited by 15 publications

(29 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Similarly, Le Yang et al (2021) [47] introduce a method to identify meaningful infrastructures (pathways or subnetworks) in cancer called FDRnet. The research addresses the heterogeneity problem in mutation by solving a Linear programming problem through use of a False Discovery Rate (FDR) parameter for budget constraint.…”

Section: Literature Reviewmentioning

confidence: 99%

Meaningful Infrastructures in Biological Networks: Related Studies, Algorithms and Evaluation Parameters

Ngobesing¹,

Atay²

2022

Preprint

View full text Add to dashboard Cite

In network science and big data, the concept of finding meaningful infrastructures in networks has emerged as a method of finding groups of entities with similar properties within very complex systems. The whole concept is generally based on finding subnetworks which have more properties (links) amongst nodes belonging to the same cluster than nodes in other groups (A concept presented by Girvan and Newman, 2002). Today meaningful infrastructure identification is applied in all types of networks from computer networks, to social networks to biological networks. In this article we will look at how meaningful infrastructure identification is applied in biological networks. This concept is important in biological networks as it helps scientist discover patterns in proteins or drugs which helps in solving many medical mysteries. This article will encompass the different algorithms that are used for meaningful infrastructure identification in biological networks. These include Genetic Algorithm, Differential Evolution, Water Cycle Algorithm (WCA), Walktrap Algorithm, Connect Intensity Iteration Algorithm (CIIA), Firefly algorithms and Overlapping Multiple Label Propagation Algorithm. These al-gorithms are compared with using performance measurement parameters such as the Mod-ularity, Normalized Mutual Information, Functional Enrichment, Recall and Precision, Re-dundancy, Purity and Surprise, which we will also discuss here.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Meaningful Infrastructures in Biological Networks: Related Studies, Algorithms and Evaluation Parameters

Ngobesing¹,

Atay²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…To have a better understanding of the specifications of UMGs, we compared their impact on the survival of cancer cell lines to that of non-driver genes selected by five other methods. Three of these methods are propagation-based and include FDRNet [42], Hierarchical HotNet (HHot-Net) [22]-in 3 different settings, and Zhou et al's propagation algorithm that resembles random walk with restart-in its original and edge-normalized settings [43]. The other two include nCOP [44], a non-propagation network method that recently demonstrated an ability to uncover non-driver genes across multiple cancer types, and MutSig [45], which identifies genes mutated more often than expected in a given cohort.…”

Section: Umgs Impact Survival Of Cancer Cells In Vitromentioning

confidence: 99%

Network propagation-based prioritization of long tail genes in 17 cancer types

Mohsen

Gunasekharan²,

Qing³

et al. 2021

Genome Biol

View full text Add to dashboard Cite

Background The diversity of genomic alterations in cancer poses challenges to fully understanding the etiologies of the disease. Recent interest in infrequent mutations, in genes that reside in the “long tail” of the mutational distribution, uncovered new genes with significant implications in cancer development. The study of cancer-relevant genes often requires integrative approaches pooling together multiple types of biological data. Network propagation methods demonstrate high efficacy in achieving this integration. Yet, the majority of these methods focus their assessment on detecting known cancer genes or identifying altered subnetworks. In this paper, we introduce a network propagation approach that entirely focuses on prioritizing long tail genes with potential functional impact on cancer development. Results We identify sets of often overlooked, rarely to moderately mutated genes whose biological interactions significantly propel their mutation-frequency-based rank upwards during propagation in 17 cancer types. We call these sets “upward mobility genes” and hypothesize that their significant rank improvement indicates functional importance. We report new cancer-pathway associations based on upward mobility genes that are not previously identified using driver genes alone, validate their role in cancer cell survival in vitro using extensive genome-wide RNAi and CRISPR data repositories, and further conduct in vitro functional screenings resulting in the validation of 18 previously unreported genes. Conclusion Our analysis extends the spectrum of cancer-relevant genes and identifies novel potential therapeutic targets.

show abstract

“…Computational methods have adopted several strategies to overcome this issue, such as gene interaction networks, used to study mutated gene’s interactions and their influence on networks 9 – 13 . Network analysis is essential, since genes affected by driver mutations tend to participate in common biological activities 14 .…”

Section: Introductionmentioning

confidence: 99%

A computational approach for the discovery of significant cancer genes by weighted mutation and asymmetric spreading strength in networks

Cutigi

Evangelista

Reis

et al. 2021

Sci Rep

View full text Add to dashboard Cite

Identifying significantly mutated genes in cancer is essential for understanding the mechanisms of tumor initiation and progression. This task is a key challenge since large-scale genomic studies have reported an endless number of genes mutated at a shallow frequency. Towards uncovering infrequently mutated genes, gene interaction networks combined with mutation data have been explored. This work proposes Discovering Significant Cancer Genes (DiSCaGe), a computational method for discovering significant genes for cancer. DiSCaGe computes a mutation score for the genes based on the type of mutations they have. The influence received for their neighbors in the network is also considered and obtained through an asymmetric spreading strength applied to a consensus gene network. DiSCaGe produces a ranking of prioritized possible cancer genes. An experimental evaluation with six types of cancer revealed the potential of DiSCaGe for discovering known and possible novel significant cancer genes.

show abstract

An efficient and effective method to identify significantly perturbed subnetworks in cancer

Cited by 15 publications

References 64 publications

Meaningful Infrastructures in Biological Networks: Related Studies, Algorithms and Evaluation Parameters

Meaningful Infrastructures in Biological Networks: Related Studies, Algorithms and Evaluation Parameters

Network propagation-based prioritization of long tail genes in 17 cancer types

A computational approach for the discovery of significant cancer genes by weighted mutation and asymmetric spreading strength in networks

Contact Info

Product

Resources

About