ArticleFrontiers in genetics2022
Gene Regulatory Identification Based on the Novel Hybrid Time-Delayed Method.
Article in Frontiers in genetics, 2022. 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.
- iProDNet: integrated probabilistic differential network inference under heterogeneous biological conditions.Briefings in bioinformatics · 2026Article
- Developing a gene expression classifier for breast cancer diagnosis.Medical & biological engineering & computing · 2025Article
- MORE interpretable multi-omic regulatory networks to characterise phenotypes.Briefings in bioinformatics · 2025Article
- scEVOLVE: cell-type incremental annotation without forgetting for single-cell RNA-seq data.Briefings in bioinformatics · 2024Article
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Authors and funding
4 authors.
Funding
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Abstract
Gene regulatory network (GRN) inference with biology data is a difficult and serious issue in the field of system biology. In order to detect the direct associations of GRN more accurately, a novel two-step GRN inference technique based on the time-delayed correlation coefficient (TDCC) and time-delayed complex-valued S-system model (TDCVSS) is proposed. First, a TDCC algorithm is utilized to construct an initial network. Second, a TDCVSS model is utilized to prune the network topology in order to delete false-positive regulatory relationships for each target gene. The complex-valued restricted additive tree and complex-valued differential evolution are proposed to approximate the optimal TDCVSS model. Finally, the overall network could be inferred by integrating the regulations of all target genes. Two real gene expression datasets from
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