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Methods for time series prediction and classification of gene regulatorynetworks (GRNs) from gene expression data have been treated separately so far.The recent emergence of attention-based recurrent neural networks (RNN) modelsboosted the interpretability of RNN parameters, making them appealing for theunderstanding of gene interactions. In this work, we generated synthetic timeseries gene expression data from a range of archetypal GRNs and we relied on adual attention RNN to predict the gene temporal dynamics. We show that theprediction is extremely accurate for GRNs with different architectures. Next,we focused on the attention mechanism of the RNN and, using tools from graphtheory, we found that its graph properties allow to hierarchically distinguishdifferent architectures of the GRN. We show that the GRNs respond differentlyto the addition of noise in the prediction by the RNN and we relate the noiseresponse to the analysis of the attention mechanism. In conclusion, this workprovides a a way to understand and exploit the attention mechanism of RNN andit paves the way to RNN-based methods for time series prediction and inferenceof GRNs from gene expression data.

Prediction of gene expression time series and structural analysis of gene regulatory networks using recurrent neural networks