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CONEXIC specifications


Unique identifier OMICS_09011
Alternative name COpy Number and EXpression In Cancer
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages Java
Computer skills Advanced
Stability Stable
Maintained No


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Publication for COpy Number and EXpression In Cancer

CONEXIC citations


Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science)

Bioinform Biol Insights
PMCID: 5824897
PMID: 29497285
DOI: 10.1177/1177932218759292

[…] or knowledge of the number of clusters. Multiple Dataset Integration uses the multinomial mixture model which requires prior knowledge of mixture probability and uses the Gibb samplings. PARADIGM and CONEXIC are 2 algorithms that use BN-based methods: the former uses EM algorithm in the computation of the unknown factor graph parameters and the latter is specifically designed for combining gene ex […]


Module Analysis Captures Pancancer Genetically and Epigenetically Deregulated Cancer Driver Genes for Smoking and Antiviral Response

PMCID: 5828545
PMID: 29331675
DOI: 10.1016/j.ebiom.2017.11.028

[…] lly exclusive genes (). In addition, Akavia et al. built further on this work and used copy number data to identify potential cancer driver genes in a modified Bayesian module network analysis called CONEXIC (). More recently, other groups are focusing on identifying driver genes through network analysis of copy number data to identify potential drivers using a Bayesian module network analysis (). […]


Transcriptome Profiling in Human Diseases: New Advances and Perspectives

Int J Mol Sci
PMCID: 5578042
PMID: 28758927
DOI: 10.3390/ijms18081652

[…] ution making use of the Bayes’ rule. Moreover, they also evaluated the specificity of methods in order to assess whether a certain method is able to analyze only two (or more) specific omics, such as Conexic [] or they can analyze any of the combinations, as iCluster []. Despite the existing methods allowing researchers to extract affordable information from the integration of multiple omic layers […]


Meta dimensional data integration identifies critical pathways for susceptibility, tumorigenesis and progression of endometrial cancer

PMCID: 5342415
PMID: 27409342
DOI: 10.18632/oncotarget.10509

[…] loped a model-based integration framework, where each data type is individually modeled and then integrated at the post-model statistical level. This framework is different from other methods such as CONEXIC [] and PARADIGM [], which are restricted to data generated from the same cohort. Rather, our model-based integration can be applied to various data sources [].We hypothesize that some pathway […]


Strategies for Integrated Analysis of Genetic, Epigenetic, and Gene Expression Variation in Cancer: Addressing the Challenges

Front Genet
PMCID: 4740898
PMID: 26870081
DOI: 10.3389/fgene.2016.00002

[…] t gene expression. The tools deviate in the types of expression-affecting variations they analyze and in their analytical approach. The strategy often depends on the assumptions of driver properties. CONEXIC assumes, in addition to the already stated assumptions, that driver mutations occur in multiple tumors more often than can be expected by chance (Akavia et al., ). It scores genes located in C […]


Methods for the integration of multi omics data: mathematical aspects

BMC Bioinformatics
PMCID: 4959355
PMID: 26821531
DOI: 10.1186/s12859-015-0857-9

[…] pproaches can be more or less stringent on the types of omics considered in input: some methods are designed to analyze a specific combination of datasets, while others are more general. For example, Conexic [] is tailored for DNA copy number variations (CNV) and gene expression data, while iCluster [] can be in principle used for the analysis of any combination of omics encoded as quantitative va […]


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CONEXIC institution(s)
Department of Biological Sciences, Columbia University, New York, NY, USA; Center for Computational Biology and Bioinformatics, Columbia University, New York, NY, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; Broad Institute of Harvard and MIT, Cambridge Center, Cambridge, MA, USA

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