OmicKriging statistics

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OmicKriging specifications

Information


Unique identifier OMICS_18028
Name OmicKriging
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License GNU General Public License version 3.0
Computer skills Advanced
Version 1.0
Stability Stable
Maintained Yes

Versioning


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Maintainer


  • person_outline Hae Kyung Im <>

Additional information


Manual: http://www.scandb.org/newinterface/tools/OmicKriging-manual.pdf Tuto: http://www.scandb.org/newinterface/tools/OmicKriging-tutorial.pdf

Publication for OmicKriging

OmicKriging in publications

 (2)
PMCID: 5181674
PMID: 27834776
DOI: 10.3233/JAD-160707

[…] the multi-modal approach used in the sub-study investigating plasma tau, did not improve predictive ability above the basic model. we used a simple additive model and more complex methods such as omickriging may be useful in this setting []. furthermore, the standard for measuring ad pathology, in particular aβ, is through pet imaging. generally, pet imaging and csf measurements are used […]

PMCID: 4937492
PMID: 27129736
DOI: 10.1534/genetics.115.185181

[…] (e.g., ). from a statistical perspective, kriging is the best linear unbiased predictor (blup) method commonly used in quantitative genetics (; ) using pedigree (, ) or dna information (g-blup) (]. omickriging is a multikernel method (, ) in which the resulting kernel is a weighted average of similarity matrices derived from different omics., although omickriging represents a promising method […]


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OmicKriging institution(s)
Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, IL, USA; Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA; Department of Health Studies, University of Chicago, Chicago, IL, USA
OmicKriging funding source(s)
Supported by the Pharmacogenetics of Anticancer Agents Research (PAAR) Group (NIH/NIGMS grant UO1GM61393), the Genotype-Tissue Expression project (GTeX) (R01 MH101820 and R01 MH090937), the University of Chicago DRTC (Diabetes Research and Training Center; P60 DK20595; P30 DK020595), the University of Chicago Cancer Center Support Grant (NCI P30 CA014599-36), the PGRN Statistical Analysis Resource (U19 HL065962), and the Conte Center grant P50MH094267; by the National Research Service Award F32CA165823 and by Award Number K12CA139160 from the National Cancer Institute.

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