Leukemia_miR: Prediction of acute leukemia phenotypes based on microRNA...
In candialab/eNetXplorer: Exploration of elastic net families for generalized linear models
Description
Usage
Format
References
Examples
Description
Data of human microRNA expression from acute myeloid (AML) and acute lymphoblastic (ALL) leukemia cell lines and primary (patient) samples. Acute lymphoblastic leukemia samples are further classified by B-cell and T-cell subphenotypes.
Usage
1
data(Leukemia_miR)
Format
A numerical matrix of predictors is provided with samples as rows and microRNAs as columns. Two categorical response vectors are provided for binomial (AML, ALL) and multinomial (AML, B-ALL, T-ALL) classification.
References
Candia,J. et al. (2015)
Uncovering low-dimensional, miR-based signatures of acute myeloid and lymphoblastic leukemias with a machine-learning-driven network approach,
Converg. Sci. Phys. Oncol. 1, 025002.
Examples
1
data(Leukemia_miR)
candialab/eNetXplorer documentation built on May 21, 2019, 12:03 p.m.
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Description Usage Format References Examples
Description
Data of human microRNA expression from acute myeloid (AML) and acute lymphoblastic (ALL) leukemia cell lines and primary (patient) samples. Acute lymphoblastic leukemia samples are further classified by B-cell and T-cell subphenotypes.
Usage
1 | data(Leukemia_miR)
|
Format
A numerical matrix of predictors is provided with samples as rows and microRNAs as columns. Two categorical response vectors are provided for binomial (AML, ALL) and multinomial (AML, B-ALL, T-ALL) classification.
References
Candia,J. et al. (2015)
Uncovering low-dimensional, miR-based signatures of acute myeloid and lymphoblastic leukemias with a machine-learning-driven network approach,
Converg. Sci. Phys. Oncol. 1, 025002.
Examples
1 | data(Leukemia_miR)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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