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

TL;DR

DeepHEN is a novel deep learning framework that predicts the essentiality of lncRNA genes by integrating sequence and network features, addressing overfitting issues and providing insights into feature contributions.

Contribution

This work introduces DeepHEN, a new model combining representation learning and graph neural networks to predict lncRNA gene essentiality, considering both sequence and network features.

Findings

DeepHEN outperforms existing methods in predicting lncRNA essentiality.

Sequence and network features both significantly influence gene essentiality.

DeepHEN effectively mitigates overfitting despite limited essential gene data.

Abstract

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.