Abstract:
BACKGROUND:Inferring gene regulatory networks (GRNs) from gene expression data remains a challenge in system biology. In past decade, numerous methods have been developed for the inference of GRNs. It remains a challenge due to the fact that the data is noisy and high dimensional, and there exists a large number of potential interactions. RESULTS:We present a novel method, namely priori-fused boosting network inference method (PFBNet), to infer GRNs from time-series expression data by using the non-linear model of Boosting and the prior information (e.g., the knockout data) fusion scheme. Specifically, PFBNet first calculates the confidences of the regulation relationships using the boosting-based model, where the information about the accumulation impact of the gene expressions at previous time points is taken into account. Then, a newly defined strategy is applied to fuse the information from the prior data by elevating the confidences of the regulation relationships from the corresponding regulators. CONCLUSIONS:The experiments on the benchmark datasets from DREAM challenge as well as the E.coli datasets show that PFBNet achieves significantly better performance than other state-of-the-art methods (Jump3, GEINE3-lag, HiDi, iRafNet and BiXGBoost).
journal_name
BMC Bioinformaticsjournal_title
BMC bioinformaticsauthors
Che D,Guo S,Jiang Q,Chen Ldoi
10.1186/s12859-020-03639-7subject
Has Abstractpub_date
2020-07-14 00:00:00pages
308issue
1issn
1471-2105pii
10.1186/s12859-020-03639-7journal_volume
21pub_type
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