ArticleInternational journal of molecular sciences2018
HSCVFNT: Inference of Time-Delayed Gene Regulatory Network Based on Complex-Valued Flexible Neural Tree Model.
Article in International journal of molecular sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 11 citations in OpenAlex.
- Diabetes-compound Relationship Identification based on Complex-valued Flexible Neural Tree and Negative Sample Selection Algorithm.Current computer-aided drug design · 2025Article
- Inference of Molecular Regulatory Systems Using Statistical Path-Consistency Algorithm.Entropy (Basel, Switzerland) · 2022Article
- Inference of Large-scale Time-delayed Gene Regulatory Network with Parallel MapReduce Cloud Platform.Scientific reports · 2018Article
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Authors and funding
6 authors at 4 institutions in 1 country.
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Abstract
Gene regulatory network (GRN) inference can understand the growth and development of animals and plants, and reveal the mystery of biology. Many computational approaches have been proposed to infer GRN. However, these inference approaches have hardly met the need of modeling, and the reducing redundancy methods based on individual information theory method have bad universality and stability. To overcome the limitations and shortcomings, this thesis proposes a novel algorithm, named HSCVFNT, to infer gene regulatory network with time-delayed regulations by utilizing a hybrid scoring method and complex-valued flexible neural network (CVFNT). The regulations of each target gene can be obtained by iteratively performing HSCVFNT. For each target gene, the HSCVFNT algorithm utilizes a novel scoring method based on time-delayed mutual information (TDMI), time-delayed maximum information coefficient (TDMIC) and time-delayed correlation coefficient (TDCC), to reduce the redundancy of regulatory relationships and obtain the candidate regulatory factor set. Then, the TDCC method is utilized to create time-delayed gene expression time-series matrix. Finally, a complex-valued flexible neural tree model is proposed to infer the time-delayed regulations of each target gene with the time-delayed time-series matrix. Three real time-series expression datasets from (Save Our Soul) SOS DNA repair system in
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