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Background:Gene regulations often change over time rather than beingconstant. But many of gene regulatory networks extracted from databases arestatic. The tumor suppressor gene $P53$ is involved in the pathogenesis of manytumors, and its inhibition effects occur after a certain period. Therefore, itis of great significance to elucidate the regulation mechanism over timepoints. Result:A qualitative method for representing dynamic gene regulatorynetwork is developed, called CdtGRN. It adopts the combination of convolutionalneural networks(CNN) and fully connected networks(DNN) as the core mechanism ofprediction. The ionizing radiation Affymetrix dataset (E-MEXP-549) was obtainedat ArrayExpress, by microarray gene expression levels predicting relationsbetween regulation. CdtGRN is tested against a time-delayed gene regulatorynetwork with $22,284$ genes related to $P53$. The accuracy of CdtGRN reaches92.07$\%$ on the classification of conservative verification set, and a kappacoefficient reaches $0.84$ and an average AUC accuracy is 94.25$\%$. Thisresulted in the construction of. Conclusion:The algorithm and program wedeveloped in our study would be useful for identifying dynamic gene regulatorynetworks, and objectively analyze the delay of the regulatory relationship byanalyzing the gene expression levels at different time points. The time-delayedgene regulatory network of $P53$ is also inferred and representedqualitatively, which is helpful to understand the pathological mechanism oftumors.

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