ArticleBMC bioinformatics2021
Reverse engineering gene regulatory network based on complex-valued ordinary differential equation model.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Double optimal transport for differential gene regulatory network inference with unpaired samples.Bioinformatics (Oxford, England) · 2025Article
- A Machine Learning Approach to Simulate Gene Expression and Infer Gene Regulatory Networks.Entropy (Basel, Switzerland) · 2023Article
- Biology and medicine in the landscape of quantum advantages.Journal of the Royal Society, Interface · 2022Review
- Accurate determination of causalities in gene regulatory networks by dissecting downstream target genes.Frontiers in genetics · 2022Article
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Authors and funding
7 authors.
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Abstract
backgroundThe growing researches of molecular biology reveal that complex life phenomena have the ability to demonstrating various types of interactions in the level of genomics. To establish the interactions between genes or proteins and understand the intrinsic mechanisms of biological systems have become an urgent need and study hotspot.
resultsIn order to forecast gene expression data and identify more accurate gene regulatory network, complex-valued version of ordinary differential equation (CVODE) is proposed in this paper. In order to optimize CVODE model, a complex-valued hybrid evolutionary method based on Grammar-guided genetic programming and complex-valued firefly algorithm is presented.
conclusionsWhen tested on three real gene expression datasets from E. coli and Human Cell, the experiment results suggest that CVODE model could improve 20-50% prediction accuracy of gene expression data, which could also infer more true-positive regulatory relationships and less false-positive regulations than ordinary differential equation.
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