RSNET: inferring gene regulatory networks by a redundancy silencing and network enhancement technique

Current gene regulatory network (GRN) inference methods are notorious for a great number of indirect interactions hidden in the predictions. Filtering out the indirect interactions from direct ones remains an important challenge in the reconstruction of GRNs. To address this issue, we developed a re...

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Bibliographic Details
Published in:BMC bioinformatics 2022-05, Vol.23 (1), p.165-165, Article 165
Main Authors: Jiang, Xiaohan, Zhang, Xiujun
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Applications software
Dependence
E coli
Experiments
Gene expression
Gene regulatory network
Gene silencing
Genetic engineering
Genetic regulation
Indirect interaction
Linear programming
Methods
Network enhancement
Network inference
Networks
Noise
Optimization
Optimization techniques
Parameter estimation
Performance evaluation
Redundancy
Redundancy silencing
Ripening
Simulation
Software
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Summary:Current gene regulatory network (GRN) inference methods are notorious for a great number of indirect interactions hidden in the predictions. Filtering out the indirect interactions from direct ones remains an important challenge in the reconstruction of GRNs. To address this issue, we developed a redundancy silencing and network enhancement technique (RSNET) for inferring GRNs. To assess the performance of RSNET method, we implemented the experiments on several gold-standard networks by using simulation study, DREAM challenge dataset and Escherichia coli network. The results show that RSNET method performed better than the compared methods in sensitivity and accuracy. As a case of study, we used RSNET to construct functional GRN for apple fruit ripening from gene expression data. In the proposed method, the redundant interactions including weak and indirect connections are silenced by recursive optimization adaptively, and the highly dependent nodes are constrained in the model to keep the real interactions. This study provides a useful tool for inferring clean networks.
ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-022-04696-w