Baoshan Ma1,*, Mingkun Fang1 and Xiangtian Jiao1
1 College of Information Science and Technology, Dalian Maritime University, Dalian 116039, China
The proposed method is a scalable method exploiting time-series and steady-state data jointly, in which nonlinear ODEs and XGBoost are employed to infer gene regulatory networks.
If you find our method is useful, please cite our paper: Baoshan Ma, Mingkun Fang, Xiangtian Jiao. Inference of gene regulatory networks based on nonlinear ordinary differential equations. Bioinformatics, 2020,36(19):4885-4893. https://doi.org/10.1093/bioinformatics/btaa032
The program of xgbgrn can combine time-series data and steady-state data to infer GRNs, the steady-state data is not necessary.
The program of xgbgrn_2 can only be applied to one type of data.
Python version=3.6
Xgboost version=0.82
scikit-learn version=l0.24.2
numpy version=1.16.3
xgbgrn:
TS_data: a matrix of time-series data
time_points: a list of time points
alpha:a constant or specify "from_data"
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
xgbgrn_2:
expr_data: a matrix of gene expression data
gene_names: a list of gene names
regulators: a list of names of regulatory genes
param: a dict of parameters of xgboost