COSINE: COndition SpecIfic sub-NEtwork

To identify the globally most discriminative subnetwork from gene expression profiles using an optimization model and genetic algorithm

AuthorHaisu Ma
Date of publication2014-07-10 07:34:03
MaintainerHaisu Ma <>
LicenseGPL (>= 2)

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Man pages

choose_lambda: Choose the most appropriate weight parameter lambda

cond.fyx: Compute the ECF-statistics measuring the differential...

COSINE-package: COndition SpecIfic subNEtwork identification

DataSimu: Simulation of the six datasets and the case dataset

diff_gen: Calculate the F-statistics and ECF-statistics

diff_gen_for3: Generate the F-statistics and ECF-statistics for the...

diff_gen_PPI: Generate the scaled node score and scaled edge score for...

f.test: To get the F-statistics for each gene

GA_search: Use genetic algorithm to search for the globally optimal...

GA_search_PPI: Run genetic algorithm to search for the PPI sub-network

get_components_PPI: Get all the components (connected clusters) of the...

get_quantiles: Get the five quantiles of the weight parameter lambda

get_quantiles_PPI: Get the five quantile values of lambda for analysis of gene...

PPI: The protein protein interaction network data

random_network_sampling_PPI: To sample random sub-network from the PPI data

scaled_edge_score: The scaled ECF statistics of all the edges

scaled_node_score: The scaled ECF-statistics of all the edges

Score_adjust_PPI: To adjust the score of the selected PPI sub-network using...

score_scaling: To get the normalzied F-statistics and ECF-statistics

set1_GA: Result of genetic algorithm search for simulated data set1

set1_scaled_diff: The standardized F-statistics and ECF-statistics for the...

set1_unscaled_diff: The unstandardized F-statistics and ECF-statistics of...

simulated_data: The simulated data sets used in the paper

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