BiXGBoost: a scalable, flexible boosting based method for reconstructing gene regulatory networks.
Authors of this article are:
Zheng R, Li M, Chen X, Wu FX, Pan Y, Wang J.
A summary of the article is shown below:
Motivation: Reconstructing gene regulatory networks (GRNs) based on gene expression profiles is still an enormous challenge in systems biology. Random forest based methods have been proved a kind of efficient methods to evaluate the importance of gene regulations. Nevertheless, the accuracy of traditional methods can be further improved. With time-series gene expression data, exploiting inherent time information and high order time lag are promising strategies to improve the power and accuracy of GRNs inference.Results: In this study, we propose a scalable, flexible approach called BiXGBoost to reconstruct GRNs. BiXGBoost is a bidirectional based method by considering both candidate regulatory genes and target genes for a specific gene. Moreover, BiXGBoost utilizes time information efficiently and integrates XGBoost to evaluate the feature importance. Randomization and regularization are also applied in BiXGBoost to address the over-fitting problem. The results on DREAM4 and Escherichia coli datasets show the good performance of BiXGBoost on different scale of networks.Availability: Our Python implementation of BiXGBoost is available at https://github.com/zrq0123/BiXGBoost.Supplementary information: Supplementary data are available at Bioinformatics online.
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