Essential proteins are important for the survival and reproduction of organisms. Many computational methods have been proposed to identify essential proteins, due to the production of vast amounts of protein-protein interaction (PPI) data. It has been demonstrated that PPI networks have graphtheoretic characteristics as so-called small-world and scale-free. The traditional metrics cannot really reflect the relationship between proteins when identifying essential proteins from PPI networks. In this paper, we construct a diffusion distance network (DSN) by combining PPI topology characteristics with orthologous proteins and sub-cellular localization information of proteins. Taking the modularity feature of essential proteins into account, we proposed a new essential proteins prediction method based on DSN. We employed our DSN method and ten other state-of-the-art methods to predict essential proteins. The precision-recall curve, jackknife methodology and so on are used to test the performance of these methods. Experimental results show that our method outperform ten other competitive methods. The row data and the software are freely available at: https://github.com/husaiccsu/DSN. INDEX TERMS Essential proteins, diffusion distance, protein-protein interaction.

The identification of essential proteins can help in understanding the minimum requirements for cell survival and development. Ever-increasing amounts of high-throughput data provide us with opportunities to detect essential proteins from protein interaction networks (PINs). Existing network-based approaches are limited by the poor quality of the underlying PIN data, which exhibits high rates of false positive and false negative results. To overcome this problem, researchers have focused on the prediction of essential proteins by combining PINs with other biological data, which has led to the emergence of various interactions between proteins. It remains challenging, however, to use aggregated multiplex interactions within a single analysis framework to identify essential proteins. In this study, we created a multiplex biological network (MON) by initially integrating PINs, protein domains, and gene expression profiles. Next, we proposed a new approach to discover essential proteins by extending the random walk with restart algorithm to the tensor, which provides a data model representation of the MON. In contrast to existing approaches, the proposed MON approach considers for the importance of nodes and the different types of interactions between proteins during the iteration. MON was implemented to identify essential proteins within two yeast PINs. Our comprehensive experimental results demonstrated that MON outperformed 11 other state-of-the-art approaches in terms of precision-recall curve, jackknife curve, and other criteria.

Amid the rapid advancement of neural machine translation, the challenge of data sparsity has been a major obstacle. To address this issue, this study proposes a general data augmentation technique for various scenarios. It examines the predicament of parallel corpora diversity and high quality in both rich- and low-resource settings, and integrates the low-frequency word substitution method and reverse translation approach for complementary benefits. Additionally, this method improves the pseudo-parallel corpus generated by the reverse translation method by substituting low-frequency words and includes a grammar error correction module to reduce grammatical errors in low-resource scenarios. The experimental data are partitioned into rich- and low-resource scenarios at a 10:1 ratio. It verifies the necessity of grammatical error correction for pseudo-corpus in low-resource scenarios. Models and methods are chosen from the backbone network and related literature for comparative experiments. The experimental findings demonstrate that the data augmentation approach proposed in this study is suitable for both rich- and low-resource scenarios and is effective in enhancing the training corpus to improve the performance of translation tasks.