GenRev statistics

Tool stats & trends

Looking to identify usage trends or leading experts?

GenRev specifications

Information


Unique identifier OMICS_06985
Name GenRev
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages Python
Computer skills Advanced
Stability No
Maintained No

Versioning


No version available

Publication for GenRev

GenRev citations

 (11)
library_books

ndmaSNF: cancer subtype discovery based on integrative framework assisted by network diffusion model

2017
Oncotarget
PMCID: 5687665
PMID: 29179495
DOI: 10.18632/oncotarget.21643

[…] We then did pathway enrichment analysis on those genes per subtype. And the top 60 potential driver genes attained from DriverNet were used for subtype-specific network module discovery via software GenRev []. The experimental results indicated that our ndmaSNF has the ability to find distinct cancer subtypes relevant to different clinical outcomes and network modules. […]

library_books

Identification of candidate genes related to pancreatic cancer based on analysis of gene co expression and protein protein interaction network

2017
Oncotarget
PMCID: 5642621
PMID: 29050346
DOI: 10.18632/oncotarget.20537

[…] oth protein interaction and gene co-expression information. To date, there are a number of methods that can be used to find the subnetworks. Here, a standalone and platform independent software named GenRev [], which is able to explore the functional relevance genes, was used to extract subnetwork. The input documents contained seed genes and common network. The Pearson's correlation coefficients […]

library_books

Analyzing the genes related to Alzheimer’s disease via a network and pathway based approach

2017
PMCID: 5406904
PMID: 28446202
DOI: 10.1186/s13195-017-0252-z

[…] orithm coincides with this biological principle, which uses a greedy heuristic strategy to iteratively link the smaller trees to larger ones until there is only one tree connecting all seed nodes []. GenRev [] was utilized to identify the pathological subnetwork from the human interactome using the curated AD-related genes as input. To assess the non-randomness of the constructed network, 1000 ran […]

library_books

Identification of breast cancer candidate genes using gene co expression and protein protein interaction information

2016
Oncotarget
PMCID: 5094985
PMID: 27150055
DOI: 10.18632/oncotarget.9132

[…] The limited k-walks algorithm is another algorithm in GenRev, which can run randomly in the network by using a Markov chain and build a relevant subnetwork connecting seed nodes []. The relevance of an edge and a node related to the seed genes is assesse […]

library_books

Deciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin Action

2015
PLoS Comput Biol
PMCID: 4470683
PMID: 26083494
DOI: 10.1371/journal.pcbi.1004202

[…] 5 and global P-value < 0.05 as the expanded genes. In the second step, we expanded the nodes from in the first step by lateral movement by applying the K-Walk method implemented in the Python package GenRev []. The K-Walk algorithm simulates random walks in the network using a Markov Chain to build the most relevant subnetwork, connecting seed nodes by walk a fixed length L or up to a maximal leng […]

library_books

Oncogenes and tumor suppressor genes: comparative genomics and network perspectives

2015
BMC Genomics
PMCID: 4474543
PMID: 26099335
DOI: 10.1186/1471-2164-16-S7-S8

[…] To better understand the interactions between OCGs and TSGs, we generated a subnetwork that contains OCGs and TSGs using the GenRev program [] (version 1.0.1). Given a network and a set of interest nodes, GenRev enables calculate a subnetwork containing the interest nodes and non-interest nodes. The interest nodes are termi […]

Citations

Looking to check out a full list of citations?

GenRev institution(s)
Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA

GenRev reviews

star_border star_border star_border star_border star_border
star star star star star

Be the first to review GenRev