iEssLnc: quantitative estimation of lncRNA gene essentialities with meta-path-guided random walks on the lncRNA-protein interaction network

Abstract

Gene essentiality is defined as the extent to which a gene is required for the survival and reproductive success of a living system. It can vary between genetic backgrounds and environments. Essential protein coding genes have been well studied. However, the essentiality of non-coding regions is rarely reported. Most regions of human genome do not encode proteins. Determining essentialities of non-coding genes is demanded. We developed iEssLnc models, which can assign essentiality scores to lncRNA genes. As far as we know, this is the first direct quantitative estimation to the essentiality of lncRNA genes. By taking the advantage of graph neural network with meta-path-guided random walks on the lncRNA-protein interaction network, iEssLnc models can perform genome-wide screenings for essential lncRNA genes in a quantitative manner. We carried out validations and whole genome screening in the context of human cancer cell-lines and mouse genome. In comparisons to other methods, which are transferred from protein-coding genes, iEssLnc achieved better performances. Enrichment analysis indicated that iEssLnc essentiality scores clustered essential lncRNA genes with high ranks. With the screening results of iEssLnc models, we estimated the number of essential lncRNA genes in human and mouse. We performed functional analysis to find that essential lncRNA genes interact with microRNAs and cytoskeletal proteins significantly, which may be of interest in experimental life sciences. All datasets and codes of iEssLnc models have been deposited in GitHub (https://github.com/yyZhang14/iEssLnc).

Deep Learning Approaches for lncRNA-Mediated Mechanisms: A Comprehensive Review of Recent Developments

“…Recent studies can approximately be categorized into three groups: The first cluster comprises methods that apply GNNs, demonstrated in [ 58 ] and [ 63 ]. They harnessed the power of GNN to predict lncRNA–protein interactions, achieving high AUC and AUPR scores.…”

“…For instance, BiHo–GNN, a bipartite graph-embedding method based on GNN, reported an impressive AUC of 0.950 and AUPR of 0.899 [ 58 ]. iEssLnc, another graph neural network, leveraged meta-path-guided random walks on the lncRNA–protein interaction network to attain an AUC of 0.912 and AUPR of 0.921 [ 63 ]. Despite the clear advantages of these methods in terms of accuracy and recall, certain disadvantages exist.…”

“…Moreover, the exploration of novel methods for quantifying lncRNA gene essentiality, such as in the study by Zhang et al [ 63 ], further expands the scope of lncRNA research. This signifies a shift toward more comprehensive studies that not only predict interactions but also investigate the implications of these interactions in gene functionality.…”

“…BiHo-GNN, using bipartite graph embedding [58]; Capsule-LPI, a multichannel capsule network for lncRNA-protein interaction prediction [59]; DeepLPI, a multimodal deep learning method for lncRNA-protein isoform interactions [60]; DFRPI, deep autoencoder and marginal Fisher analysis [61]; EnANNDeep, ensemble-based framework with adaptive k-nearest neighbor for the lncRNA-protein interaction [62]; iEssLnc, graph neural network-based estimation of lncRNA gene essentiality [63];…”

Recent advances in the characterization of essential genes and development of a database of essential genes

“…Additionally, predictive models have emerged for essential genes in the noncoding regions. Zhang et al developed an iEssLnc model using metapath‐guided random walks, which was the first estimation model for the essentiality of lncRNA genes [ 115 ]. It can be inferred that “good” data and efficient machine learning techniques are required for accurate prediction.…”