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Gene essentiality refers to the degree to which a gene is necessary for thesurvival and reproductive efficacy of a living organism. Although theessentiality of non-coding genes has been documented, there are still aspectsof non-coding genes' essentiality that are unknown to us. For example, We donot know the contribution of sequence features and network spatial features toessentiality. As a consequence, in this work, we propose DeepHEN that couldanswer the above question. By buidling a new lncRNA-proteion-protein networkand utilizing both representation learning and graph neural network, wesuccessfully build our DeepHEN models that could predict the essentiality oflncRNA genes. Compared to other methods for predicting the essentiality oflncRNA genes, our DeepHEN model not only tells whether sequence features ornetwork spatial features have a greater influence on essentiality but alsoaddresses the overfitting issue of those methods caused by the low number ofessential lncRNA genes, as evidenced by the results of enrichment analysis.

DeepHEN: quantitative prediction essential lncRNA genes and rethinking essentialities of lncRNA genes