Abstract
BackgroundEssential proteins are crucial for cellular life and thus, identification of essential proteins is an important topic and a challenging problem for researchers. Recently lots of computational approaches have been proposed to handle this problem. However, traditional centrality methods cannot fully represent the topological features of biological networks. In addition, identifying essential proteins is an imbalanced learning problem; but few current shallow machine learning-based methods are designed to handle the imbalanced characteristics.ResultsWe develop DeepEP based on a deep learning framework that uses the node2vec technique, multi-scale convolutional neural networks and a sampling technique to identify essential proteins. In DeepEP, the node2vec technique is applied to automatically learn topological and semantic features for each protein in protein-protein interaction (PPI) network. Gene expression profiles are treated as images and multi-scale convolutional neural networks are applied to extract their patterns. In addition, DeepEP uses a sampling method to alleviate the imbalanced characteristics. The sampling method samples the same number of the majority and minority samples in a training epoch, which is not biased to any class in training process. The experimental results show that DeepEP outperforms traditional centrality methods. Moreover, DeepEP is better than shallow machine learning-based methods. Detailed analyses show that the dense vectors which are generated by node2vec technique contribute a lot to the improved performance. It is clear that the node2vec technique effectively captures the topological and semantic properties of PPI network. The sampling method also improves the performance of identifying essential proteins.ConclusionWe demonstrate that DeepEP improves the prediction performance by integrating multiple deep learning techniques and a sampling method. DeepEP is more effective than existing methods.
Highlights
Essential proteins are crucial for cellular life and identification of essential proteins is an important topic and a challenging problem for researchers
We propose a new deep learning framework, DeepEP, which is used for identifying essential proteins
DeepEP aims to investigate whether deep learning and sampling methods could achieve notable improvements for identifying essential proteins
Summary
Essential proteins are crucial for cellular life and identification of essential proteins is an important topic and a challenging problem for researchers. Lots of computational approaches have been proposed to handle this problem. Identifying essential proteins is an imbalanced learning problem; but few current shallow machine learning-based methods are designed to handle the imbalanced characteristics. Identifying essential proteins is very important for researchers. There are several biological experimental methods to identify essential proteins, such. Jeong et al [6] proposed centrality-lethality rule which point out that the highly connected proteins in a PPI network tend to be essential. Based on this rule, a lot of centrality methods have been
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