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