CdtGRN: Construction of qualitative time-delayed gene regulatory networks with a deep learning method

TL;DR

This paper introduces CdtGRN, a deep learning-based method combining CNN and DNN to construct qualitative, time-delayed gene regulatory networks, specifically applied to P53-related genes, revealing dynamic regulatory mechanisms over time.

Contribution

It presents a novel deep learning approach for constructing qualitative, time-delayed gene regulatory networks, addressing the limitation of static network models.

Findings

Achieved 92.07% accuracy in classifying gene regulation relations.

Constructed a qualitative time-delayed network for P53 involving over 22,000 genes.

Demonstrated the method's usefulness in understanding tumor-related gene regulation mechanisms.

Abstract

Background:Gene regulations often change over time rather than being constant. But many of gene regulatory networks extracted from databases are static. The tumor suppressor gene $P53$ is involved in the pathogenesis of many tumors, and its inhibition effects occur after a certain period. Therefore, it is of great significance to elucidate the regulation mechanism over time points. Result:A qualitative method for representing dynamic gene regulatory network is developed, called CdtGRN. It adopts the combination of convolutional neural networks(CNN) and fully connected networks(DNN) as the core mechanism of prediction. The ionizing radiation Affymetrix dataset (E-MEXP-549) was obtained at ArrayExpress, by microarray gene expression levels predicting relations between regulation. CdtGRN is tested against a time-delayed gene regulatory network with $22,284$ genes related to $P53$. The accuracy of CdtGRN reaches 92.07$\%$ on the classification of conservative verification set, and a kappa coefficient reaches $0.84$ and an average AUC accuracy is 94.25$\%$. This resulted in the construction of. Conclusion:The algorithm and program we developed in our study would be useful for identifying dynamic gene regulatory networks, and objectively analyze the delay of the regulatory relationship by analyzing the gene expression levels at different time points. The time-delayed gene regulatory network of $P53$ is also inferred and represented qualitatively, which is helpful to understand the pathological mechanism of tumors.