DeepHE: Accurately predicting human essential genes based on deep learning

Abstract

Accurately predicting essential genes using computational methods can greatly reduce the effort in finding them via wet experiments at both time and resource scales, and further accelerate the process of drug discovery. Several computational methods have been proposed for predicting essential genes in model organisms by integrating multiple biological data sources either via centrality measures or machine learning based methods. However, the methods aiming to predict human essential genes are still limited and the performance still need improve. In addition, most of the machine learning based essential gene prediction methods are lack of skills to handle the imbalanced learning issue inherent in the essential gene prediction problem, which might be one factor affecting their performance. We propose a deep learning based method, DeepHE, to predict human essential genes by integrating features derived from sequence data and protein-protein interaction (PPI) network. A deep learning based network embedding method is utilized to automatically learn features from PPI network. In addition, 89 sequence features were derived from DNA sequence and protein sequence for each gene. These two types of features are integrated to train a multilayer neural network. A cost-sensitive technique is used to address the imbalanced learning problem when training the deep neural network. The experimental results for predicting human essential genes show that our proposed method, DeepHE, can accurately predict human gene essentiality with an average performance of AUC higher than 94%, the area under precision-recall curve (AP) higher than 90%, and the accuracy higher than 90%. We also compare DeepHE with several widely used traditional machine learning models (SVM, Naïve Bayes, Random Forest, and Adaboost) using the same features and utilizing the same costsensitive technique to against the imbalanced learning issue. The experimental results show that DeepHE significantly outperforms the compared machine learning models. We have demonstrated that human essential genes can be accurately predicted by designing effective machine learning algorithm and integrating representative features captured from available biological data. The proposed deep learning framework is effective for such task.

EPGAT: Gene Essentiality Prediction With Graph Attention Networks

“…were at least comparable (when not superior) to node2vec, a state-of-the-art method for gene or protein essentiality prediction (used, for instance, in [31], [33], [34]). While maintaining a slight edge, EPGAT still has the benefits of having a more straightforward training procedure and shorter training time.…”

“…Finally, we compared our results against models built upon network features extracted by the node2vec (N2V) embedding method, as applied in previous works (e.g., [31], [33], [34]). N2V uses biased random walks on graphs to learn a graph embedding for each node, which captures the network structure in a regular format.…”

“…More recently, Zhang et al [34] proposed DeepHE, a deep learning-based method to predict human essential genes. DeepHE integrates sequence features extracted from DNA and protein sequences with features learned from PPI network using node2vec.…”

An iteration model for identifying essential proteins by combining comprehensive PPI network with biological information

“…Machine learning has also been applied in the field of essential protein identification. By using features from DNA and protein sequence data, Zhang et al proposed a deep learning-based network embedding method to automatically learn features and use the features to train deep neural networks to predict human essential genes [36]. Zeng et al proposed the Ess-NEXG model, which used RNA-seq, subcellular localization, orthology and other information to construct a reliable weighting network, and captured topological features through node2vec, and finally used a classifier to make predictions [37].…”

A Computational Framework Based on Ensemble Deep Neural Networks for Essential Genes Identification

“…Lloyd et al successfully discovered the distinguishing features of the Arabidopsis thaliana genome that are helpful for building within-and cross-species predictors, and they subsequently applied this model to detect essential genes in the Oryza sativa and S. cerevisiae genomes [26]. Additionally, a significant contribution was made by Zhang et al [27], who combined both sequence-and network-based properties to identify essential genes and found a valuable result. In this research, a deep learning-based model was implemented to learn the features derived from sequence data and protein-protein interaction networks.…”