GBM                     Calculate Gene Regulatory Network from
                        Expression data using either LS-TreeBoost or
                        LAD-TreeBoost
GBM.test                Test GBM predictor
GBM.train               Train GBM predictor
RGBM                    Regularized Gradient Boosting Machine for
                        inferring GRN
RGBM.test               Test rgbm predictor
RGBM.train              Train RGBM predictor
add_names               Add row and column names to the adjacency
                        matrix A
apply_row_deviation     Apply row-wise deviation on the inferred GRN
consider_previous_information
                        Remember the intermediate inferred GRN while
                        generating the final inferred GRN
first_GBM_step          Perform either LS-Boost or LAD-Boost ('GBM') on
                        expression matrix E followed by the
                        'null_model_refinement_step'
get_colids              Get the indices of recitifed list of Tfs for
                        individual target gene
get_filepaths           Generate filepaths to maintain adjacency
                        matrices and images
get_ko_experiments      Get indices of experiments where knockout or
                        knockdown happened
get_tf_indices          Get the indices of all the TFs from the data
normalize_matrix_colwise
                        Column normalize the obtained adjacency matrix
null_model_refinement_step
                        Perform the null model refinement step
regularized_GBM_step    Perform the regularized GBM modelling once the
                        initial GRN is inferred
regulate_regulon_size   Regulate the size of the regulon for each TF
second_GBM_step         Re-iterate through the core GBM model building
                        with optimal set of Tfs for each target gene
select_ideal_k          Identifies the optimal value of k i.e. top k
                        Tfs for each target gene
test_regression_stump_R
                        Test the regression model
train_regression_stump_R
                        Train the regression stump
transform_importance_to_weights
                        Log transforms the edge-weights in the inferred
                        GRN
v2l                     Convert adjacency matrix to a list of edges
z_score_effect          Generates a matrix S2 of size Ntfs x Ntargets
                        using the null-mutant zscore algorithm 'Prill,
                        Robert J., et al'
