CMI2NI statistics

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CMI2NI specifications

Information


Unique identifier OMICS_19285
Name CMI2NI
Software type Application/Script
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux
Programming languages MATLAB
Computer skills Advanced
Stability Stable
Maintained Yes

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Maintainer


  • person_outline Guanghua Xiao <>

Publication for CMI2NI

CMI2NI in publications

 (3)
PMCID: 5655205
PMID: 29113310
DOI: 10.18632/oncotarget.21268

[…] (dbncs) based on dynamic bayesian network (dbn) to construct the multiple time-delayed grns for the first time, combining the comprehensive score (cs) with the dbn model. dbncs algorithm first uses cmi2ni (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. then the redundant regulations […]

PMCID: 4865039
PMID: 27171286
DOI: 10.1371/journal.pone.0154953

[…] to infer a causal network by conditional relation [, , , ]. combining pc algorithm with cmi and corrected-cmi, pca-cmi (path consistency algorithm based on conditional mutual information) [] and cmi2ni (cmi2-based network inference) [] are proposed to “thin” the edges with independent correlation recursively from zero to high order correlation. theoretical analysis shows that cmi […]

PMCID: 4357691
PMID: 25539927
DOI: 10.1093/nar/gku1315

[…] the problems, we propose a novel concept, namely conditional mutual inclusive information (cmi2), to describe the regulations between genes. furthermore, with cmi2, we develop a new approach, namely cmi2ni (cmi2-based network inference), for reverse-engineering grns. in cmi2ni, cmi2 is used to quantify the mutual information between two genes given a third one through calculating […]


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CMI2NI institution(s)
Department of Statistics, Inha University, Incheon, Korea; Department of Statistics, Seoul National University, Seoul, Korea; Department of Statistical Science, Southern Methodist University, Dallas, TX, USA; Department of Biostatistics, University of Florida, Gainesville, FL, USA; Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
CMI2NI funding source(s)
Supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science, ICT and Future Planning (NRF-2015R1C1A1A02036312 and NRF-2011-0030810), and the National Institutes of Health grants (1R01CA17221, 1R01GM117597, R15GM113157).

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