Essential genes are a group of genes that are indispensable for cell survival and cell fertility. Studying human essential genes helps scientists reveal the underlying biological mechanisms of a human cell but also guides disease treatment. Recently, the publication of human essential gene data makes it possible for researchers to train a machine-learning classifier by using some features of the known human essential genes and to use the classifier to predict new human essential genes. Previous studies have found that the essentiality of genes closely relates to their properties in the protein–protein interaction (PPI) network. In this work, we propose a novel supervised method to predict human essential genes by network embedding the PPI network. Our approach implements a bias random walk on the network to get the node network context. Then, the node pairs are input into an artificial neural network to learn their representation vectors that maximally preserves network structure and the properties of the nodes in the network. Finally, the features are put into an SVM classifier to predict human essential genes. The prediction results on two human PPI networks show that our method achieves better performance than those that refer to either genes’ sequence information or genes’ centrality properties in the network as input features. Moreover, it also outperforms the methods that represent the PPI network by other previous approaches.

Identifying driver genes contributing to the occurrence and development of cancers plays a critical role in cancer research and treatment. Some recent computational approaches identify cancer-driver genes based on gene networks, assuming that cancer-driver genes perform essential functions in gene networks. Due to the noise in gene function networks, many works focus on integrating gene networks derived from multi-omics datasets to improve the accuracy of cancer driver gene detection. However, most of them ignore the information interactions between these multi-omics datasets. In this work, we propose MNGCL, a Multi-Network Graph Contrastive Learning method to identify cancer driver genes. It first constructs three gene networks as different views based on protein interactions, gene semantic similarities, and gene co-occurrence in signaling pathways. Then, we perform data augmentation of these gene networks and input them into a graph contrastive learning (GCL) encoder with shared parameters to learn consistent gene feature representation in different networks from a holistic perspective. After that, the gene features from the GCL encoder are passed through three different graph convolutional networks to generate the unique gene feature representations in the three networks. Finally, we used a logistic regression model to fuse the gene feature representations generated in each network to predict cancer driver genes. The experimental results show that MNGCL improves the area under the ROC curve (AUC) and the area under the precision-recall curve (AUPRC) to a greater extent than the existing methods in identifying driver genes for both pan-cancer and single-type cancers. Furthermore, the ablation studies show that our model capturing dependencies and interactions between gene networks provided a more comprehensive perspective on the molecular mechanisms underlying cancer and improved the accuracy of cancer driver identification.

Essential genes play an indispensable role in supporting the life of an organism. Identification of essential genes helps us to understand the underlying mechanism of cell life. The essential genes of bacteria are potential drug targets of some diseases genes. Recently, several computational methods have been proposed to detect essential genes based on the static protein–protein interactive (PPI) networks. However, these methods have ignored the fact that essential genes play essential roles under certain conditions. In this work, a novel method was proposed for the identification of essential proteins by fusing the dynamic PPI networks of different time points (called by FDP). Firstly, the active PPI networks of each time point were constructed and then they were fused into a final network according to the networks’ similarities. Finally, a novel centrality method was designed to assign each gene in the final network a ranking score, whilst considering its orthologous property and its global and local topological properties in the network. This model was applied on two different yeast data sets. The results showed that the FDP achieved a better performance in essential gene prediction as compared to other existing methods that are based on the static PPI network or that are based on dynamic networks.

Cancer subtype classification helps us to understand the pathogenesis of cancer and develop new cancer drugs, treatment from which patients would benefit most. Most previous studies detect cancer subtypes by extracting features from individual samples, ignoring their associations with others. We believe that the interactions of cancer samples can help identify cancer subtypes. This work proposes a cancer subtype classification method based on a residual graph convolutional network and a sample similarity network. First, we constructed a sample similarity network regarding cancer gene co-expression patterns. Then, the gene expression profiles of cancer samples as initial features and the sample similarity network were passed into a two-layer graph convolutional network (GCN) model. We introduced the initial features to the GCN model to avoid over-smoothing during the training process. Finally, the classification of cancer subtypes was obtained through a softmax activation function. Our model was applied to breast invasive carcinoma (BRCA), glioblastoma multiforme (GBM) and lung cancer (LUNG) datasets. The accuracy values of our model reached 82.58%, 85.13% and 79.18% for BRCA, GBM and LUNG, respectively, which outperformed the existing methods. The survival analysis of our results proves the significant clinical features of the cancer subtypes identified by our model. Moreover, we can leverage our model to detect the essential genes enriched in gene ontology (GO) terms and the biological pathways related to a cancer subtype.