DeepHEN: quantitative prediction essential lncRNA genes and rethinking essentialities of lncRNA genes
This work addresses the challenge of understanding and predicting essential non-coding genes for biologists and computational researchers, though it appears incremental as it builds on existing methods with specific improvements.
The researchers tackled the problem of predicting essential lncRNA genes by developing DeepHEN, a model that uses representation learning and graph neural networks on a new lncRNA-protein-protein network, achieving results that address overfitting issues and reveal the relative influence of sequence and network spatial features on essentiality.
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.