LogMiNeR statistics

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Associated diseases

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LogMiNeR specifications

Information


Unique identifier OMICS_23336
Name LogMiNeR
Alternative name Logistic Multiple Network-constrained Regression
Software type Framework/Library
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
Computer skills Advanced
Version 0.2
Stability Stable
Maintained Yes

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Maintainers


  • person_outline Steven Kleinstein <>
  • person_outline Stefan Avey <>

Publication for Logistic Multiple Network-constrained Regression

LogMiNeR in publications

 (2)
PMCID: 5870750
PMID: 28881994
DOI: 10.1093/bioinformatics/btx260

[…] incorporating different types of biological knowledge produces models that highlight distinct aspects of the underlying biology, while maintaining predictive accuracy. we propose a new framework, logistic multiple network-constrained regression (logminer), and apply it to understand the mechanisms underlying differential responses to influenza vaccination. although standard logistic […]

PMCID: 5287479
PMID: 28146585
DOI: 10.1371/journal.pone.0170793

[…] to the selection of appropriate concepts. finally, the last recognized criterion is the exclusion of specific concepts related to particular fields, such as oracle flashback transaction; oracle logminer; oracle tools; sqledit; mysql utilities; and so on., therefore, in accordance with the findings of previous studies [–], and [], the authors manually extracted concepts from each model […]


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LogMiNeR institution(s)
Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Section of Infectious Diseases, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA; Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA; Departments of Pathology and Immunobiology, Yale School of Medicine, New Haven, CT, USA
LogMiNeR funding source(s)
Supported by the National Institutes of Health [grant number U19AI089992]; by K24 AG042489; and by the National Science Foundation Graduate Research Fellowship Program [grant number DGE-1122492].

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