iLSGRN: Inference of large-scale gene regulatory networks based on multi-model fusion

1 College of Information Science and Technology, Dalian Maritime University, Dalian 116039, China

iLSGRN is a large-scale gene regulatory network inference method based on multi model fusion, which includes dimension reduction using maximum mutual information coefficient and feature fusion of XGBoost and RF machine learning models.

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The version of Python and packages

Python version 3.8.5 minepy 1.2.5 numpy 1.20.3 pandas 1.2.4 scikit-learn 0.24.2 scipy 1.6.3 xgboost 1.4.2

Parameters Description

    alpha:a constant of gene decay rate
    param: a dict of parameters of xgboost
    threshold: Threshold of maximum mutual information coefficient dimension reduction
    xgb_learning_rate: Learning rate of xgboost
	
case_size100:
    TS_data: a matrix of time-series data
    time_points: a list of time points
    SS_data: a matrix of time-series data, the default is "none"
    gene_names: a list of gene names
    regulators: a list of names of regulatory genes, the default is "all", 
    param: a dict of parameters of xgboost and RF