XGBoost-Based Gene Network Inference Method for Steady-State Data


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    Inferring gene regulatory networks (GRNs) from steady gene expression data remains a challenge in systems biology. There are a large number of potential direct or indirect regulatory relationships that are difficult to be identified by traditional methods. To address this issue, we propose a new method based on boosting integrated model, and apply randomization and regularization to solve the model over fitting problem. For the inconsistent weights from different subproblems, we integrate normalization and statistical methods to deal with the initial weights. Using the benchmark datasets from DREAM5 challenges, it shows that our method achieves better performance than other state-of-the-art methods. In the simulated data set generated by in-silico, the two evaluation indicators of area under precision-recall curves (AUPR) and area under receiver operating characteristic (AUROC) are significantly better than existing methods, and the accuracy is higher in the real experimental data of two organisms, E.coli and S.cerevisiae. Especially for AUROC, the indicators are higher than the existing best methods.

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CHE Dandan, GUO Shun, JIANG Qingshan. XGBoost-Based Gene Network Inference Method for Steady-State Data[J]. Journal of Integration Technology,2020,9(2):50-59

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  • Online: March 25,2020
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