期刊
BIOINFORMATICS
卷 35, 期 11, 页码 1893-1900出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/bty908
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资金
- National Natural Science Foundation of China [61622213, 61732009, 61420106009]
- 111 Project [B18059]
- Fundamental Research Funds for the Central Universities of Central South University [2018zzts028]
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.
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