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  4. Vol 9, Issue 1, 2025

Gene Regulatory Network Inference Based on Convolutional GRU

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DOI: 10.23977/acss.2025.090111 | Downloads: 17 | Views: 513

Author(s)

Siying Du 1

Affiliation(s)

1 School of Information, Yunnan Normal University, Kunming, China

Corresponding Author

Siying Du

ABSTRACT

Time-course single-cell RNA sequencing (scRNA-seq) data reflect gene expression changes over time, offering a valuable resource for exploring dynamic gene interactions and building dynamic gene regulatory networks (GRNs). However, most existing methods are typically designed for bulk RNA sequencing (bulk RNA-seq) data and cannot be directly applied to time-course scRNA-seq data. Addressing this issue, we present CGGRN, an approach based on convolutional gated recurrent unit (GRU) for inferring GRNs. CGGRN transforms time-course data into images, including raw pairwise gene images and neighborhood images, and aggregates them with time point information into a four-dimensional tensor. The tensor is then fed into the convolutional GRU to capture features for each gene pair and reconstruct the GRN. We conducted trials on four time-course scRNA-seq datasets using CGGRN, and the outcomes show that CGGRN surpasses existing models in constructing GRN.

KEYWORDS

Convolutional GRU, Gene Regulatory Network, Deep Learning

CITE THIS PAPER

Siying Du, Gene Regulatory Network Inference Based on Convolutional GRU. Advances in Computer, Signals and Systems (2025) Vol. 9: 74-78. DOI: http://dx.doi.org/10.23977/acss.2025.090111.

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