Inference of gene regulatory networks for overcoming low performance in real-world data

The identification of gene regulatory networks is important for understanding the mechanisms of various biological phenomena. Many methods have been proposed to infer networks from time-series gene expression data obtained by high-throughput next-generation sequencings. Such methods can effectively infer gene regulatory networks for in silico data, but inferring the networks accurately from in vivo data remiains a challenge because of the large noise and low time sampling rate. Here, we proposed a novel unsupervised learning method, Multi-view attention Long-short term memory for Network inference (MaLoN). It can infer gene regulatory networks with temporal changes in gene regulation using the multi-view attention Long Short-term memory model. Using in vivo benchmark datasets in Saccharomyces cerevisiae and Escherichia coli, we showed that MaLoN can infer gene regulatory networks more accurately than existing methods. The ablated models indicated that the multi-view attention mechanism suppressed false positives. The order of activation of gene regulations inferred by MaLoN was consistent with existing knowledge.