Gene Regulatory Network Reconstruction from Yeast Expression Time Series

Zheng, Ming; Zhuo, Mugui

doi:10.1007/978-981-13-3648-5_191

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2020

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“…e simulation dataset is selected from the yeast cell gene chip data [24,25] provided by Spellman et al in Stanford University, from which 6 genes are extracted to form a small gene regulation network. e data are as shown in Table 1.…”

Section: Simulation Datasetmentioning

confidence: 99%

Research on Multi-Time-Delay Gene Regulation Network Based on Fuzzy Label Propagation

Zhang

2020

Journal of Healthcare Engineering

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In view of time delay existing in gene regulation, by using the analysis idea and methods of complex network, this paper proposes a multi-time-delay gene regulation network analysis method based on the fuzzy label propagation. The algorithm takes the relative change trend coefficient, the correlation coefficient, and the mutual information as the similarity measurement indexes for the gene pair, fully reflecting the correlation of gene pairs and simultaneously obtaining the gene regulation relationship and the time delay through the fuzzy label propagation algorithm of the semisupervised learning. Experimental results of the cell cycle-regulated genes of yeast show that the proposed construction method of GRN can not only correctly select potential regulation genes but also provide details about the gene regulator model, thereby more accurately constructing gene regulation network.

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Section: Simulation Datasetmentioning

confidence: 99%

Research on Multi-Time-Delay Gene Regulation Network Based on Fuzzy Label Propagation

Zhang

2020

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

Gene Regulatory Network Reconstruction from Yeast Expression Time Series

Cited by 1 publication

References 9 publications

Research on Multi-Time-Delay Gene Regulation Network Based on Fuzzy Label Propagation

Research on Multi-Time-Delay Gene Regulation Network Based on Fuzzy Label Propagation

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