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Multi-Dendrix specifications

Information


Unique identifier OMICS_06146
Name Multi-Dendrix
Alternative name Multiple Pathways De novo Driver Exclusivity
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages Python
Computer skills Advanced
Stability Stable
Maintained Yes

Versioning


No version available

Documentation


Publication for Multiple Pathways De novo Driver Exclusivity

Multi-Dendrix citations

 (10)
library_books

Computational Methods for Characterizing Cancer Mutational Heterogeneity

2017
Front Genet
PMCID: 5469877
PMID: 28659971
DOI: 10.3389/fgene.2017.00083

[…] e set and the mutual exclusivity of mutations in the set, and uses a Markov Chain Monte Carlo (MCMC) approach for identifying mutually exclusive gene sets altered in a large fraction of the patients. Multi-Dendrix (Leiserson et al., ) employs the same score as Dendrix and extends it to multiple sets, and uses an integer linear program (ILP) based algorithm to simultaneously find multiple sets of m […]

library_books

Identification of driver modules in pan cancer via coordinating coverage and exclusivity

2017
Oncotarget
PMCID: 5482642
PMID: 28415609
DOI: 10.18632/oncotarget.16433

[…] MCMC algorithm, the BLP method is more efficient. A multi-objective optimization model based on a Genetic Algorithm (MOGA) was introduced to adjust the trade-off between coverage and exclusivity []. Multi-Dendrix [] designed a new metric as the sum of Dendrix weights and adopted a new programming model to identify multiple modules simultaneously. CoMDP proposed an exact mathematical programming m […]

library_books

Discovery of cancer common and specific driver gene sets

2017
Nucleic Acids Res
PMCID: 5449640
PMID: 28168295
DOI: 10.1093/nar/gkx089

[…] tion among different biological pathways and functional modules may shed new lights on the understanding of the cellular mechanisms underlying carcinogenesis. Leiserson et al. () generalized Dendrix (Multi-Dendrix) to simultaneously identify multiple driver gene sets in cancer. More importantly, the collaboration among different pathways means these gene sets are likely simultaneously mutated in a […]

library_books

Computational approaches for the identification of cancer genes and pathways

2016
Wiley Interdiscip Rev Syst Biol Med
PMCID: 5215607
PMID: 27863091
DOI: 10.1002/wsbm.1364

[…] nce, MEMo integrates different data sources and combines different methodologies (Tables and ). Methods that address the limitation due to rare mutations are Dendrix (De novo Driver Exclusivity) and Multi‐Dendrix. Dendrix identifies driver pathways by their high exclusivity and high sample coverage. Unlike RME, Dendrix requires high coverage of the discovered gene modules instead of each gene sep […]

library_books

An Evolutionary Approach for Identifying Driver Mutations in Colorectal Cancer

2015
PLoS Comput Biol
PMCID: 4575033
PMID: 26379039
DOI: 10.1371/journal.pcbi.1004350

[…] nscript isoforms, variation in mutation types, different background mutation rates, redundancy of the genetic code and others and used a likelihood ratio test to obtain significance levels. Moreover, Multi-Dendrix, DriverNet, MuSiC, and MEMo [, , , –] were also developed to identify driver pathways using network-based approaches. An integrated meta-analysis using multiple methods can be accessed t […]

library_books

High Prevalence and Clinical Relevance of Genes Affected by Chromosomal Breaks in Colorectal Cancer

2015
PLoS One
PMCID: 4574474
PMID: 26375816
DOI: 10.1371/journal.pone.0138141

[…] ins and is recruited in case of DNA damage response [,].To further address the biological relevance of recurrent breakpoint genes we tried to construct modules of putative cancer driving genes, using Multi-Dendrix. On the one hand, this analysis can reveal oncogenic pathways by looking for mutually exclusive gene mutation patterns that cover nearly all CRC samples. On the other hand, if an apparen […]

Citations

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Multi-Dendrix institution(s)
Department of Computer Science and Center for Computational Molecular Biology, Brown University, Providence, RI, USA

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