RGBM v1.0-7

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by Raghvendra Mall

LS-TreeBoost and LAD-TreeBoost for Gene Regulatory Network Reconstruction

Provides an implementation of Regularized LS-TreeBoost & LAD-TreeBoost algorithm for Regulatory Network inference from any type of expression data (Microarray/RNA-seq etc). See Mall et al (2017) <doi:10.1101/132670>.

Functions in RGBM

Name Description
GBM.train
RGBM
RGBM.test
RGBM.train
get_ko_experiments
get_tf_indices
consider_previous_information
first_GBM_step
get_colids
get_filepaths
test_regression_stump_R
train_regression_stump_R
normalize_matrix_colwise
null_model_refinement_step
transform_importance_to_weights
add_names
apply_row_deviation
regularized_GBM_step
regulate_regulon_size
GBM
GBM.test
second_GBM_step
select_ideal_k
z_score_effect
v2l
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Details

Type Package
Date 2017-05-07
Repository CRAN
License GPL (>= 3)
LazyLoad yes
NeedsCompilation yes
Packaged 2017-05-08 06:15:02 UTC; rmall
Date/Publication 2017-05-08 11:59:35 UTC

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