HyperModules: identifying clinically and phenotypically significant network modules with disease mutations for biomarker discovery

Leung, Alvin Chung Man; Bader, Gary D.; Reimand, Jüri

doi:10.1093/bioinformatics/btu172

Cited by 29 publications

(29 citation statements)

References 19 publications

(23 reference statements)

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“…The HyperModules method 47 identifies subnetworks with cancer mutations that are maximally correlated with clinical characteristics such as patient survival, tumor stage, or relapse. This tool can also be used to study tumor subtypes by extracting subnetworks whose mutations are significantly enriched in a particular subtype.…”

Section: Major Types Of Pathway and Network Analysis Techniquesmentioning

confidence: 99%

Pathway and network analysis of cancer genomes

et al. 2015

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Genomic information on tumors from 50 cancer types catalogued by The International Cancer Genome Consortium (ICGC) shows that only few well-studied driver genes are frequently mutated, in contrast to many infrequently mutated genes that may also contribute to tumor biology. Hence there has been large interest in developing pathway and network analysis methods that group genes and illuminate the processes involved. We provide an overview of these analysis techniques and show where they guide mechanistic and translational investigations.

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Section: Major Types Of Pathway and Network Analysis Techniquesmentioning

confidence: 99%

Pathway and network analysis of cancer genomes

et al. 2015

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“…Among them, community structure has been recognized as an important bridge to connect the topological structures and functional modules. It has been widely studied in various real-world networks such as the Internet, the word wild web, epidemiology, metabolism, ecosystems [33,35,39,47,48]. Here, we introduce the community structure into the disease-gene prediction problem, aiming to further improve the performance of the network-based disease-gene prediction.…”

Section: Community-based Similarity (Cs)mentioning

confidence: 99%

Predicting disease-related genes by path structure and community structure in protein–protein networks

Tang

et al. 2018

J. Stat. Mech.

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Network-based computational approaches to predict unknown genes associated with certain diseases are of considerable significance for uncovering the molecular basis of human diseases. In this paper, we proposed a kind of new disease-gene-prediction methods by combining the path-based similarity with the community structure in the human protein-protein interaction network. Firstly, we introduced a set of path-based similarity indices, a novel community-based similarity index, and a new similarity combining the path-based similarity index. Then we assessed the statistical significance of the measures in distinguishing the disease genes from non-disease genes, to confirm their availability in predicting disease genes. Finally, we applied these measures to the disease-gene prediction of single disease-gene family, and analyzed the performance of these measures in disease-gene prediction, especially the effect of the community structure on the prediction performance in detail. The results indicated that genes associated with the same or similar diseases commonly reside in the same community of the protein-protein interaction network, and the community structure is greatly helpful for the disease-gene prediction.

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“…In the literature, a growing number of methods have been reported for network-based survival analysis [31][32][33][34][35][36]. Some of them have been implemented as tools, including Net-Cox [31], Reactome FI [32], HyperModules [36] and HotNet [35].…”

Section: Resultsmentioning

confidence: 99%

“…Some of them have been implemented as tools, including Net-Cox [31], Reactome FI [32], HyperModules [36] and HotNet [35]. Among these, all but Reactome FI are conceptually similar to dnet in using survival data to guide survival network discovery, while HyperModules currently does not support the Cox regression required in this application.…”

Section: Resultsmentioning

confidence: 99%

The `dnet¿ approach promotes emerging research on cancer patient survival

Fang¹,

Gough²

2014

Genome Medicine

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We present the 'dnet' package and apply it to the 'TCGA' mutation and clinical data of >3,000 patients. We uncover the existence of an underlying gene network that at least partially controls cancer 'survivalness', with mutations that are significantly correlated with patient survival, yet independent of tumour origin and type. The survivalness network has natural community structure corresponding to tumour hallmarks, and contains genes that are potentially druggable in the clinic. This network has evolutionary roots in Deuterostomia identifying PTK2 and VAV1 as under-valued relative to more studied genes from that era. The 'dnet' R package is available at http://cran.r-project.org/package=dnet.

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HyperModules: identifying clinically and phenotypically significant network modules with disease mutations for biomarker discovery

Cited by 29 publications

References 19 publications

Pathway and network analysis of cancer genomes

Pathway and network analysis of cancer genomes

Predicting disease-related genes by path structure and community structure in protein–protein networks

The `dnet¿ approach promotes emerging research on cancer patient survival

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