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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.

If you find our method is useful, please cite our paper:

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