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

arXiv: https://arxiv.org/abs/2309.10008 · HTML: https://mlsanity.com/paper/2309.10008/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 18 Sep 2023.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Protein Representation Learning by Geometric Structure Pretraining](https://mlsanity.com/paper/2203.06125/) (2022)
- [dna2vec: Consistent vector representations of variable-length k-mers](https://mlsanity.com/paper/1701.06279/) (2017)
- [Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning](https://mlsanity.com/paper/2301.05931/) (2023)

## Abstract (arXiv)

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.