A Deep Learning Framework for Predicting Human Essential Genes by Integrating Sequence and Functional data

Abstract

MotivationEssential genes are necessary to the survival or reproduction of a living organism. The prediction and analysis of gene essentiality can advance our understanding to basic life and human diseases, and further boost the development of new drugs. Wet lab methods for identifying essential genes are often costly, time consuming, and laborious. As a complement, computational methods have been proposed to predict essential genes by integrating multiple biological data sources. Most of these methods are evaluated on model organisms. However, prediction methods for human essential genes are still limited and the relationship between human gene essentiality and different biological information still needs to be explored. In addition, exploring suitable deep learning techniques to overcome the limitations of traditional machine learning methods and improve the prediction accuracy is also important and interesting.ResultsWe propose a deep learning based method, DeepSF, to predict human essential genes. DeepSF integrates four types of features, that is, sequence features, features from gene ontology, features from protein complex, and network features. Sequence features are derived from DNA and protein sequence for each gene. 100 GO terms from cellular component are used to form a feature vector for each gene, in which each component captures the relationship between a gene and a GO term. Network features are learned from protein-protein interaction (PPI) network using a deep learning based network embedding method. The features derived from protein complexes capture the relationships between a gene or a gene’s direct neighbors from PPI network and protein complexes. The four types of features are integrated together to train a multilayer neural network. The experimental results of 10-fold cross validation show that DeepSF can accurately predict human gene essentiality with an average performance of AUC about 94.35%, the area under precision-recall curve (auPRC) about 91.28%, the accuracy about 91.35%, and the F1 measure about 77.79%. In addition, the comparison results show that DeepSF significantly outperforms several widely used traditional machine learning models (SVM, Random Forest, and Adaboost), and performs slightly better than a recent deep learning model (DeepHE).ConclusionsWe have demonstrated that the proposed method, DeepSF, is effective for predicting human essential genes. Deep learning techniques are promising at both feature learning and classification levels for the task of essential gene prediction.Availability and ImplementationThe python code will be freely available at https://github.com/xzhang2016/DeepSF.

DeEPsnap: human essential gene prediction by integrating multi-omics data

“…Grover et al proposed a network embedding method based on deep learning, node2vec, to learn a low-dimension representation for each node [22]. This method has been used to extract topological features from PPI networks for predicting essential genes, and these features are more informative than those obtained via some popular centrality measures [7, 23, 24, 25]. CNN was used to extract local patterns from time-serial gene expression profiles from S. cerevisiae [23] and Zeng et al also used bidirectional long short-term memory (LSTM) cells to extract features from the same gene expression profiles [24].…”