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.

通过建立一个新的lncRNA-蛋白质-蛋白质网络，并利用表示学习和图神经网络的方法，我们成功构建了DeepHEN模型，能够预测lncRNA基因的重要性，同时解决了其它方法由于基本lncRNA基因数量较少导致的过拟合问题。

DeepHEN: 高效预测必需的长链非编码RNA基因并重新思考长链非编码RNA基因的重要性